This project focuses on developing a real-time object detection system utilizing Convolutional Neural Networks (CNNs) to accurately identify and locate objects within images or video feeds. The CNN model is trained on a diverse dataset to detect multiple object classes with high precision, leveraging layers designed for feature extraction and pattern recognition. The system is optimized for deployment on edge devices, allowing it to function efficiently in resource-constrained environments, making it suitable for applications in IoT, security surveillance, and autonomous systems. With pre-trained and fine-tuned models, this project provides a robust and adaptable solution for various real-world object detection tasks.