The study of cell behavior and dynamics is of paramount importance in various fields, including cell biology, medicine, and biotechnology. Manual cell tracking, the traditional method for monitoring cellular movements, is labor-intensive, time-consuming, and prone to human error. Consequently, the integration of computer vision and image analysis techniques has emerged as a powerful solution to automate cell tracking and provide more accurate and efficient results. This research paper explores the state-of-the-art approaches in the integration of computer vision and image analysis for automated cell tracking. We review the fundamental concepts of computer vision, image processing, and machine learning as they pertain to cell tracking applications. Additionally, we discuss the challenges and opportunities presented by complex cell behaviors, diverse imaging modalities, and noisy data. The paper highlights key methodologies for cell detection, segmentation, and tracking, including deep learning-based techniques, feature extraction methods, and hybrid approaches that combine various algorithms to achieve robust and reliable cell tracking. We delve into the importance of data preprocessing, model training, and performance evaluation in achieving accurate cell tracking results. Furthermore, we discuss the practical implications of automated cell tracking, including its potential impact on drug discovery, disease diagnosis, and understanding fundamental biological processes. We also address the ethical considerations and limitations of automated cell tracking systems. Through this comprehensive exploration, we aim to provide researchers and practitioners in the fields of cell biology and image analysis with a foundational understanding of the integration of computer vision techniques for automated cell tracking. Our findings contribute to the advancement of technology-driven cellular studies and pave the way for innovative applications that leverage automated cell tracking to enhance scientific discovery and medical breakthroughs