diff --git a/src/ai/gan.py b/src/ai/gan.py
new file mode 100644
index 000000000..23868ea8d
--- /dev/null
+++ b/src/ai/gan.py
@@ -0,0 +1,55 @@
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import layers
+
+class GAN:
+    def __init__(self, noise_dim, data_shape):
+        self.noise_dim = noise_dim
+        self.data_shape = data_shape
+        self.generator = self.build_generator()
+        self.discriminator = self.build_discriminator()
+        self.gan = self.build_gan()
+
+    def build_generator(self):
+        model = tf.keras.Sequential([
+            layers.Dense(128, activation='relu', input_dim=self.noise_dim),
+            layers.Dense(256, activation='relu'),
+            layers.Dense(np.prod(self.data_shape), activation='tanh'),
+            layers.Reshape(self.data_shape)
+        ])
+        return model
+
+    def build_discriminator(self):
+        model = tf.keras.Sequential([
+            layers.Flatten(input_shape=self.data_shape),
+            layers.Dense(256, activation='relu'),
+            layers.Dense(128, activation='relu'),
+            layers.Dense(1, activation='sigmoid')
+        ])
+        return model
+
+    def build_gan(self):
+        self.discriminator.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+        self.discriminator.trainable = False
+        model = tf.keras.Sequential([self.generator, self.discriminator])
+        model.compile(loss='binary_crossentropy', optimizer='adam')
+        return model
+
+    def train(self, real_data, epochs=10000, batch_size=128):
+        for epoch in range(epochs):
+            # Train Discriminator
+            idx = np.random.randint(0, real_data.shape[0], batch_size)
+            real_samples = real_data[idx]
+            noise = np.random.normal(0, 1, (batch_size, self.noise_dim))
+            fake_samples = self.generator.predict(noise)
+
+            d_loss_real = self.discriminator.train_on_batch(real_samples, np.ones((batch_size, 1)))
+            d_loss_fake = self.discriminator.train_on_batch(fake_samples, np.zeros((batch_size, 1)))
+            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
+
+            # Train Generator
+            noise = np.random.normal(0, 1, (batch_size, self.noise_dim))
+            g_loss = self.gan.train_on_batch(noise, np.ones((batch_size, 1)))
+
+            if epoch % 1000 == 0:
+                print(f"{epoch} [D loss: {d_loss[0]:.4f}, acc.: {100 * d_loss[1]:.2f}] [G loss: {g_loss:.4f}]")