The integration of artificial intelligence into police investigative interviewing is emerging as a pivotal development in modern law enforcement. Investigative interviewing, defined as the systematic process of extracting accurate and comprehensive accounts from victims, witnesses, and suspects, remains the cornerstone of criminal investigations and judicial proceedings. As case backlogs grow and the complexity of crimes escalates, law enforcement agencies are increasingly exploring AI-driven tools to streamline the collection and analysis of testimonial evidence. Current AI applications in this domain focus on automated transcription, sentiment analysis, lie detection algorithms, and cognitive load assessment. Proponents argue that these technologies can enhance interview consistency, reduce investigator bias, and accelerate the identification of investigative leads. Machine learning models trained on established interviewing protocols can provide real-time guidance to officers, ensuring adherence to best practices and minimizing procedural errors that could compromise admissibility in court. However, the deployment of AI in sensitive interrogative settings raises substantial ethical and legal questions. Critics emphasize the risk of algorithmic bias, particularly when training data lacks demographic diversity, potentially leading to discriminatory outcomes or wrongful suspicion. The reliability of microexpression and physiological monitoring systems remains scientifically contested, with numerous peer-reviewed studies questioning their validity in legal contexts. Furthermore, the automation of investigative interviews may undermine the human element of policing, where empathy, contextual understanding, and adaptive questioning are often decisive in obtaining credible testimony. Judicial bodies and regulatory agencies are responding with caution. Several jurisdictions have issued preliminary guidelines requiring transparency in AI decision-making, mandatory human oversight, and rigorous validation before deployment in investigative workflows. Privacy advocates stress the need for strict data governance to protect the rights of interviewees, particularly regarding biometric and psychological data collection. The trajectory of AI-assisted investigative interviewing hinges on balancing technological efficiency with constitutional safeguards and scientific rigor. As pilot programs expand and performance metrics are standardized, law enforcement will need to establish clear frameworks for accountability, explainability, and continuous auditing. While AI holds promise for improving the accuracy and consistency of witness and suspect interviews, its successful integration will depend on cross-disciplinary collaboration among technologists, legal experts, and criminal justice practitioners to ensure that innovation serves rather than supersedes the principles of fair and thorough investigation.