AI code review tools leverage machine learning models and code quality metrics to analyze and evaluate code, offering benefits like increased developer productivity, better team efficiency, and support for multiple programming languages. These tools, such as CodeGuru, Codacy, Snyk, CodeScene, and CodeRabbit, provide feedback, identify security vulnerabilities, and suggest improvements by integrating with development workflows and supporting various coding environments. While they excel in automating repetitive tasks and maintaining code consistency, limitations include potential false positives and negatives, adherence to predefined rules, and a lack of comprehensive understanding of complex systems compared to human reviewers. Each tool has distinct features and strengths, such as CodeGuru's integration with AWS and focus on security, Codacy's support for over 40 programming languages, Snyk's real-time scanning capabilities, CodeScene's emphasis on technical debt, and CodeRabbit's continuous context-aware feedback. Despite their limitations, AI tools serve as valuable assistants in code review processes, complementing human efforts by enhancing efficiency and helping maintain high-quality, secure, and reliable code.