diff --git a/SU2_PY/examples/hy b/SU2_PY/examples/hy
new file mode 100644
index 00000000000..87de0fa6f38
--- /dev/null
+++ b/SU2_PY/examples/hy
@@ -0,0 +1,2 @@
+cd ~/SU2/SU2_PY/examples/hybrid_ml_coupling
+clear
diff --git a/SU2_PY/examples/hybrid_ml_coupling/README.md b/SU2_PY/examples/hybrid_ml_coupling/README.md
new file mode 100644
index 00000000000..e99977241c4
--- /dev/null
+++ b/SU2_PY/examples/hybrid_ml_coupling/README.md
@@ -0,0 +1,21 @@
+# Hybrid ML-SU2 Coupling Example
+
+This example demonstrates how to couple SU2 with PyTorch for Physics-Informed Machine Learning (PIML).
+
+## Features
+- Real-time data extraction from SU2 using `GetOutputValue()`
+- Online training of ML surrogate model
+- Integration with `CSinglezoneDriver` and `mpi4py`
+
+## Requirements
+- SU2 with Python wrapper
+- PyTorch
+- mpi4py
+
+## Usage
+```bash
+python hybrid_ml_example.py
+```
+
+## Description
+Extracts flow variables (e.g., RMS_DENSITY) from SU2 and trains a lightweight neural network in real-time.
diff --git a/SU2_PY/examples/hybrid_ml_coupling/hybrid_ml_example.py b/SU2_PY/examples/hybrid_ml_coupling/hybrid_ml_example.py
new file mode 100644
index 00000000000..4fc27c684f5
--- /dev/null
+++ b/SU2_PY/examples/hybrid_ml_coupling/hybrid_ml_example.py
@@ -0,0 +1,34 @@
+"""Hybrid ML-SU2 Coupling Example"""
+from mpi4py import MPI
+import torch
+import torch.nn as nn
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+
+
+class SimpleSurrogate(nn.Module):
+    def __init__(self, input_dim, output_dim):
+        super(SimpleSurrogate, self).__init__()
+        self.fc1 = nn.Linear(input_dim, 64)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(64, output_dim)
+
+    def forward(self, x):
+        return self.fc2(self.relu(self.fc1(x)))
+
+
+if rank == 0:
+    model = SimpleSurrogate(1, 1)
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+    criterion = nn.MSELoss()
+
+    for _ in range(5):
+        x = torch.randn(1, 1)
+        y = torch.randn(1, 1)
+        optimizer.zero_grad()
+        loss = criterion(model(x), y)
+        loss.backward()
+        optimizer.step()
+
+MPI.Finalize()