diff --git a/CHANGELOG.md b/CHANGELOG.md
index e036489..f6c0bfa 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Added comprehensive graph validation utilities (`wmg.validator`) for ensuring data integrity:
+  - Geometric consistency: Verify 3D coordinates lie on unit sphere (L2 norm ≈1.0)
+  - Metadata alignment: Cross-reference metadata.json specs with tensor shapes
+  - Connectivity health: Detect isolated nodes that could break message passing
+  - Hierarchical integrity: Validate inter-level edge mappings in multi-level meshes
+  Includes command-line interface and PyTorch-optimized validation for large graphs.
+  @Prince637-boo
+
 - Added a standalone graph consistency checking tool (`wmg.diagnostics.check_graph_consistency`) to ensure structural health, such as verifying all grid nodes successfully connect to the mesh (#42).
 - Add Django-style graph filtering via `filter_graph`, for example to select
   nodes by type (`node__type="mesh"`), edges by component
diff --git a/src/weather_model_graphs/__init__.py b/src/weather_model_graphs/__init__.py
index 273d4db..8724947 100644
--- a/src/weather_model_graphs/__init__.py
+++ b/src/weather_model_graphs/__init__.py
@@ -5,7 +5,7 @@
 except importlib.metadata.PackageNotFoundError:
     __version__ = "unknown"
 
-from . import create, save, visualise
+from . import create, save, visualise, validator
 from .filtering import filter_graph
 from .networkx_utils import (
     replace_node_labels_with_unique_ids,
diff --git a/src/weather_model_graphs/validator.py b/src/weather_model_graphs/validator.py
new file mode 100644
index 0000000..1ef878e
--- /dev/null
+++ b/src/weather_model_graphs/validator.py
@@ -0,0 +1,344 @@
+"""
+Graph validation utilities for weather-model-graphs.
+
+This module provides comprehensive validation for graph integrity, including:
+- Geometric consistency of 3D coordinates
+- Metadata alignment with tensor shapes
+- Connectivity health checks
+- Hierarchical integrity for multi-level meshes
+"""
+
+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple, Any
+import warnings
+
+import numpy as np
+import torch
+import networkx as nx
+
+try:
+    import torch_geometric as pyg
+    HAS_PYG = True
+except ImportError:
+    HAS_PYG = False
+    warnings.warn("PyTorch Geometric not available, some validations may be limited")
+
+
+def validate_geometric_consistency(mesh_positions: List[torch.Tensor], tolerance: float = 1e-6) -> bool:
+    """
+    Check that all 3D Cartesian coordinates sit on a unit sphere (L2 norm ≈1.0).
+
+    Parameters
+    ----------
+    mesh_positions : List[torch.Tensor]
+        List of mesh position tensors, one per level for hierarchical graphs.
+    tolerance : float
+        Tolerance for norm deviation from 1.0.
+
+    Returns
+    -------
+    bool
+        True if all positions are on unit sphere within tolerance.
+    """
+    all_valid = True
+    for level, pos in enumerate(mesh_positions):
+        if pos.shape[1] < 3:
+            warnings.warn(f"Level {level}: Expected at least 3D coordinates, got {pos.shape[1]}D")
+            all_valid = False
+            continue
+
+        # Only check the first 3 dimensions (x, y, z coordinates)
+        coords = pos[:, :3]
+        norms = torch.norm(coords, dim=1)
+        deviations = torch.abs(norms - 1.0)
+        max_deviation = deviations.max().item()
+
+        if max_deviation > tolerance:
+            warnings.warn(
+                f"Level {level}: Max norm deviation from 1.0 is {max_deviation:.2e} "
+                f"(tolerance: {tolerance}). "
+                f"Found {torch.sum(deviations > tolerance).item()} invalid positions."
+            )
+            all_valid = False
+
+    return all_valid
+
+
+def validate_metadata_alignment(metadata_path: str, graph_tensors: Dict[str, Any]) -> bool:
+    """
+    Cross-reference metadata.json specs with actual tensor shapes in .pt files.
+
+    Parameters
+    ----------
+    metadata_path : str
+        Path to metadata.json file.
+    graph_tensors : Dict[str, Any]
+        Dictionary of loaded graph tensors.
+
+    Returns
+    -------
+    bool
+        True if shapes align with metadata specs.
+    """
+    try:
+        with open(metadata_path, 'r') as f:
+            metadata = json.load(f)
+    except FileNotFoundError:
+        warnings.warn(f"Metadata file not found: {metadata_path}")
+        return False
+
+    all_valid = True
+
+    # Check hierarchy
+    expected_levels = metadata.get('hierarchy', {}).get('levels', 1)
+    actual_levels = len(graph_tensors.get('m2m_edge_index', []))
+
+    if actual_levels != expected_levels:
+        warnings.warn(
+            f"Hierarchy levels mismatch: expected {expected_levels}, got {actual_levels}"
+        )
+        all_valid = False
+
+    # Check edge features
+    edge_specs = metadata.get('edge_features', {})
+    for edge_type, expected_features in edge_specs.items():
+        if edge_type not in graph_tensors:
+            warnings.warn(f"Missing edge type in tensors: {edge_type}")
+            all_valid = False
+            continue
+
+        features_tensor = graph_tensors.get(f'{edge_type}_features')
+        if features_tensor is None:
+            warnings.warn(f"Missing features for edge type: {edge_type}")
+            all_valid = False
+            continue
+
+        # For hierarchical, features is a list
+        if isinstance(features_tensor, list):
+            for level, feat in enumerate(features_tensor):
+                if feat.shape[1] != len(expected_features):
+                    warnings.warn(
+                        f"{edge_type} level {level}: expected {len(expected_features)} features, "
+                        f"got {feat.shape[1]}"
+                    )
+                    all_valid = False
+        else:
+            if features_tensor.shape[1] != len(expected_features):
+                warnings.warn(
+                    f"{edge_type}: expected {len(expected_features)} features, "
+                    f"got {features_tensor.shape[1]}"
+                )
+                all_valid = False
+
+    return all_valid
+
+
+def validate_connectivity_health(graph_tensors: Dict[str, Any]) -> bool:
+    """
+    Identify isolated nodes or "dead-end" components that could break message passing.
+
+    Uses PyTorch operations for efficiency with large graphs.
+
+    Parameters
+    ----------
+    graph_tensors : Dict[str, Any]
+        Dictionary of loaded graph tensors.
+
+    Returns
+    -------
+    bool
+        True if no connectivity issues found.
+    """
+    all_valid = True
+
+    # Check mesh-to-mesh connectivity
+    m2m_edge_index = graph_tensors.get('m2m_edge_index', [])
+    if isinstance(m2m_edge_index, list) and m2m_edge_index:
+        for level, edges in enumerate(m2m_edge_index):
+            if hasattr(edges, 'shape') and edges.shape[1] > 0:
+                # Get unique nodes in this level's edges
+                present_nodes = torch.unique(edges.flatten())
+                # Estimate total nodes (approximate, since we don't have exact count)
+                max_node_idx = edges.max().item()
+                if len(present_nodes) < max_node_idx + 1:
+                    warnings.warn(
+                        f"M2M level {level}: Found {len(present_nodes)} connected nodes "
+                        f"out of expected ~{max_node_idx + 1}"
+                    )
+                    all_valid = False
+
+    # Check grid-to-mesh connectivity
+    g2m_edge_index = graph_tensors.get('g2m_edge_index')
+    if g2m_edge_index is not None and hasattr(g2m_edge_index, 'shape'):
+        if g2m_edge_index.shape[1] > 0:
+            # Grid nodes are sources (row 0), mesh nodes are destinations (row 1)
+            grid_nodes = torch.unique(g2m_edge_index[0])
+            mesh_nodes = torch.unique(g2m_edge_index[1])
+
+            # Check for isolated grid nodes (no outgoing g2m edges)
+            # This is more complex to check efficiently without full graph reconstruction
+            # For now, just warn if no g2m edges at all
+            if len(grid_nodes) == 0:
+                warnings.warn("No grid-to-mesh connections found")
+                all_valid = False
+
+    return all_valid
+
+
+def validate_hierarchical_integrity(graph_tensors: Dict[str, Any]) -> bool:
+    """
+    Validate that inter-level edges correctly map between Li and Li+1 for multi-level meshes.
+
+    Parameters
+    ----------
+    graph_tensors : Dict[str, Any]
+        Dictionary of loaded graph tensors.
+
+    Returns
+    -------
+    bool
+        True if hierarchical structure is valid.
+    """
+    mesh_up_edge_index = graph_tensors.get('mesh_up_edge_index')
+    mesh_down_edge_index = graph_tensors.get('mesh_down_edge_index')
+
+    if mesh_up_edge_index is None or mesh_down_edge_index is None:
+        # Not hierarchical, skip
+        return True
+
+    all_valid = True
+
+    # Convert to numpy for easier analysis
+    if hasattr(mesh_up_edge_index, 'cpu'):
+        mesh_up = mesh_up_edge_index.cpu().numpy()
+        mesh_down = mesh_down_edge_index.cpu().numpy()
+    else:
+        mesh_up = mesh_up_edge_index
+        mesh_down = mesh_down_edge_index
+
+    # Basic shape checks
+    if mesh_up.shape[0] != 2 or mesh_down.shape[0] != 2:
+        warnings.warn("Inter-level edge indices should have shape (2, N_edges)")
+        all_valid = False
+
+    # Check that up and down are inverses (bidirectional connectivity)
+    # mesh_up edges are expected to connect fine -> coarse, while mesh_down
+    # edges connect coarse -> fine. The edge pairs should therefore be inverse.
+    up_set = set(zip(mesh_up[0], mesh_up[1]))
+    down_set = set(zip(mesh_down[0], mesh_down[1]))
+    down_inverted = {(dst, src) for src, dst in down_set}
+
+    # Check for symmetry (up should be the inverse of down)
+    symmetric = up_set == down_inverted
+    if not symmetric:
+        warnings.warn("Inter-level edges are not inverse between mesh_up and mesh_down")
+        all_valid = False
+
+    # Check for isolated nodes in inter-level connectivity
+    all_nodes_in_up = set(mesh_up.flatten())
+    all_nodes_in_down = set(mesh_down.flatten())
+
+    if all_nodes_in_up != all_nodes_in_down:
+        warnings.warn("Node sets differ between up and down inter-level edges")
+        all_valid = False
+
+    return all_valid
+
+
+def validate_graph_directory(graph_dir: str, metadata_path: str = None) -> Dict[str, bool]:
+    """
+    Run all validations on a graph directory.
+
+    Parameters
+    ----------
+    graph_dir : str
+        Path to directory containing graph .pt files.
+    metadata_path : str, optional
+        Path to metadata.json file. If None, looks for metadata.json in graph_dir.
+
+    Returns
+    -------
+    Dict[str, bool]
+        Validation results for each check.
+    """
+    if metadata_path is None:
+        metadata_path = os.path.join(graph_dir, 'metadata.json')
+
+    # Load graph tensors (simplified version of load_graph from neural-lam)
+    graph_tensors = {}
+    required_files = [
+        'mesh_features.pt',
+        'm2m_edge_index.pt',
+        'g2m_edge_index.pt',
+        'm2g_edge_index.pt',
+        'm2m_features.pt',
+        'g2m_features.pt',
+        'm2g_features.pt'
+    ]
+
+    for filename in required_files:
+        filepath = os.path.join(graph_dir, filename)
+        if os.path.exists(filepath):
+            try:
+                graph_tensors[filename.replace('.pt', '')] = torch.load(filepath, weights_only=True)
+            except Exception as e:
+                warnings.warn(f"Failed to load {filename}: {e}")
+
+    # Optional hierarchical files
+    hierarchical_files = [
+        'mesh_up_edge_index.pt',
+        'mesh_down_edge_index.pt',
+        'mesh_up_features.pt',
+        'mesh_down_features.pt'
+    ]
+
+    for filename in hierarchical_files:
+        filepath = os.path.join(graph_dir, filename)
+        if os.path.exists(filepath):
+            try:
+                graph_tensors[filename.replace('.pt', '')] = torch.load(filepath, weights_only=True)
+            except Exception as e:
+                warnings.warn(f"Failed to load {filename}: {e}")
+
+    results = {}
+
+    # Geometric consistency
+    mesh_features = graph_tensors.get('mesh_features', [])
+    results['geometric_consistency'] = validate_geometric_consistency(mesh_features)
+
+    # Metadata alignment
+    if os.path.exists(metadata_path):
+        results['metadata_alignment'] = validate_metadata_alignment(metadata_path, graph_tensors)
+    else:
+        warnings.warn(f"Metadata file not found: {metadata_path}")
+        results['metadata_alignment'] = False
+
+    # Connectivity health
+    results['connectivity_health'] = validate_connectivity_health(graph_tensors)
+
+    # Hierarchical integrity
+    results['hierarchical_integrity'] = validate_hierarchical_integrity(graph_tensors)
+
+    return results
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Validate weather model graphs")
+    parser.add_argument("graph_dir", help="Directory containing graph .pt files")
+    parser.add_argument("--metadata", help="Path to metadata.json file")
+
+    args = parser.parse_args()
+
+    results = validate_graph_directory(args.graph_dir, args.metadata)
+
+    print("Validation Results:")
+    for check, passed in results.items():
+        status = "PASS" if passed else "FAIL"
+        print(f"  {check}: {status}")
+
+    all_passed = all(results.values())
+    print(f"\nOverall: {'PASS' if all_passed else 'FAIL'}")
\ No newline at end of file
diff --git a/tests/test_validator.py b/tests/test_validator.py
new file mode 100644
index 0000000..5fae7fb
--- /dev/null
+++ b/tests/test_validator.py
@@ -0,0 +1,88 @@
+import torch
+import pytest
+from weather_model_graphs.validator import (
+    validate_geometric_consistency,
+    validate_connectivity_health,
+    validate_hierarchical_integrity,
+)
+
+def test_spherical_consistency_valid():
+    """Test that points perfectly on the unit sphere pass validation."""
+    # Create points perfectly on the sphere (Norm = 1)
+    # Example: Cardinal points [1,0,0], [0,1,0], [0,0,1]
+    valid_pos = torch.tensor([
+        [1.0, 0.0, 0.0],
+        [0.0, 1.0, 0.0],
+        [0.0, 0.0, 1.0]
+    ], dtype=torch.float32)
+
+    assert validate_geometric_consistency([valid_pos]) is True
+
+def test_spherical_consistency_invalid():
+    """Test that points not on the unit sphere fail validation."""
+    # One point is offset (Norm != 1)
+    invalid_pos = torch.tensor([
+        [1.0, 0.0, 0.0],
+        [2.0, 2.0, 2.0]  
+    ], dtype=torch.float32)
+
+    # We expect the validator to return False
+    assert validate_geometric_consistency([invalid_pos]) is False
+
+def test_connectivity_health_valid():
+    """Test that fully connected graphs pass connectivity validation."""
+    # Create a fully connected graph with 3 nodes
+    edge_index = torch.tensor([
+        [0, 0, 1, 1, 2, 2],
+        [1, 2, 0, 2, 0, 1]
+    ])  # All pairs connected
+    graph_tensors = {
+        'm2m_edge_index': [edge_index]
+    }
+
+    assert validate_connectivity_health(graph_tensors) is True
+
+def test_hierarchical_integrity_valid():
+    """Test that inverse up/down edge sets pass validation."""
+    mesh_up = torch.tensor([
+        [0, 1],
+        [2, 3],
+    ])
+    mesh_down = torch.tensor([
+        [2, 3],
+        [0, 1],
+    ])
+    graph_tensors = {
+        'mesh_up_edge_index': mesh_up,
+        'mesh_down_edge_index': mesh_down,
+    }
+
+    assert validate_hierarchical_integrity(graph_tensors) is True
+
+
+def test_hierarchical_integrity_invalid_orientation():
+    """Test that non-inverse hierarchical edges fail validation."""
+    mesh_up = torch.tensor([
+        [0, 1],
+        [2, 3],
+    ])
+    mesh_down = torch.tensor([
+        [0, 1],
+        [2, 4],
+    ])
+    graph_tensors = {
+        'mesh_up_edge_index': mesh_up,
+        'mesh_down_edge_index': mesh_down,
+    }
+
+    assert validate_hierarchical_integrity(graph_tensors) is False
+def test_connectivity_health_invalid():
+    """Test that graphs with isolated nodes fail connectivity validation."""
+    # Create edges that reference node 3
+    edge_index = torch.tensor([[3, 3], [4, 5]])  
+    graph_tensors = {
+        'm2m_edge_index': [edge_index]
+    }
+
+    # The validator should detect that nodes 0,1,2 are missing
+    assert validate_connectivity_health(graph_tensors) is False
\ No newline at end of file