AI code review uses machine learning and large language models to automatically scan code for security vulnerabilities, style inconsistencies, and performance issues — providing fixes and explanations without waiting for human reviewers. Key benefits include consistent enforcement of coding standards, faster feedback loops for junior developers, and earlier detection of technical debt. The underlying technologies include static analysis, dynamic analysis (DAST), rule-based linters, and LLMs that understand code context. Practical adoption involves choosing a stack-compatible tool, configuring team-specific coding standards, integrating into IDEs or PR workflows, and tracking quality metrics. Despite the advantages, over-reliance, false positives/negatives, and lack of project-specific context remain challenges — making human oversight essential throughout the process.