The study employs a combination of YOLO (You Only Look Once) and Deep SORT (Simple Online and Realtime Tracking) to establish robust methodologies for Object detection and tracking. Through a systematic examination and comparative analysis of various models, including RNN (Recurrent Neural Net- work), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), CNN (Convolutional Neural Network), ResNet (Residual Network), Transformer, and others, the primary objective is to enhance the precision of trajectory prediction. The specific focus is on refining the forecasting of future Object positions by leveraging historical data. The research optimizes these models across diverse scenarios, offering a comprehensive approach to challenges in trajectory forecasting. By exploring the refined interactions between YOLO, Deep SORT, and different recurrent neural network architectures, the findings not only advance the understanding of these technologies but also provide valuable insights into their practical applications for real-time Object tracking systems across various contexts within the field of computer vision.