Snake detection is vital for promptly alerting nearby individuals when a snake is detected. Conventional approaches based on visual observation are usually time-consuming and inaccurate. This research introduces an automated deep learning-based snake detection system with Convolutional Neural Networks for real-time detection. Convolutional Neural Network is a group of layers optimized to process visual information effectively by learning spatial hierarchies across layers and hence is best suited for image classification. The model employs a 16-layer CNN architecture with Max Pooling, Dropout, and Fully Connected layers to promote stable learning. The Adam optimizer is applied to make training more efficient, and overfitting is avoided by using early stopping and model checkpointing. The system achieves 94.39% testing accuracy in identifying images as either snake or non-snake.