A critical stage in software development, code review guarantees the quality, security, and maintainability of the code. Traditional bottlenecks in the development cycle and are laborious and prone to human error. Code reviews are now more accurate and efficient thanks to automated solutions brought about by the development of artificial intelligence (AI). AI-powered tools evaluate source code, identify defects, pinpoint security flaws, and suggest fixes using Machine Learning (ML) and Natural Language Processing (NLP) techniques. This study investigates the role of AI in automated code review systems by examining several AI-driven tools and frameworks utilized in contemporary software development. We assess the efficacy of deep learning-based and conventional static analysis methods in detecting anomalies in the code. According to experimental findings, AIenhanced review systems outperform traditional techniques in terms of accuracy, efficiency, and fewer false positives. In this paper author demonstrates how AIpowered code review tools may improve software quality and developer productivity. Lastly, discussion of the shortcomings of existing AI models and propose ways to enhance automated code review procedures in the future, such as integrating AI with human-in-the-loop techniques to create more effective feedback systems.