The Advanced Object Detection System is an innovative deep learning solution designed to accurately detect and identify objects in images. Utilizing convolutional neural networks (CNNs), the system automatically learns features from a diverse set of annotated images, enabling precise object detection and classification. Built on popular deep learning frameworks such as TensorFlow or PyTorch, the system incorporates advanced CNN architectures like the Single Shot Multibox Detector (SSD) to optimize performance in real-time scenarios. Key features include transfer learning and fine-tuning, which accelerate training and improve the system's ability to handle challenges such as objects of varying sizes, poses, and occlusions. By integrating pre- trained models on large datasets like ImageNet, the system quickly learns to identify and classify objects with high accuracy. The model is rigorously tested using benchmark datasets such as COCO, with mean average precision (mAP) used to evaluate performance. The system detects objects in real-time and, using a trained dataset, identifies and classifies them based on learned features. This approach ensures efficient and reliable object detection models suited to a variety of applications. Keywords: Object Detection, Convolutional Neural Networks (CNNs), Deep Learning, Supervised Learning, Feature Extraction, Real-time Detection, CNN, SSD (Single Shot MultiBox Detector), Transfer Learning, Pre- trained Models, PyTorch, MobileNet, COCO Dataset.