The integration of artificial intelligence (AI) in forensic science is transforming investigative methodologies by enhancing efficiency, accuracy, and predictive capabilities. AI-driven forensic techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), facilitate the rapid analysis of evidence, detection of patterns, and minimization of human error. This paper explores the current applications of AI in forensic science, covering digital forensics, biometric analysis, predictive policing, and forensic DNA examination. Additionally, it highlights emerging advancements such as deepfake detection, behavioral analysis, and blockchain-based evidence authentication. While AI presents unparalleled opportunities for forensic investigations, its implementation raises critical challenges, including algorithmic bias, ethical concerns, data security risks, and legal admissibility. This study emphasizes the need for robust technical frameworks, regulatory standards, and ethical considerations to ensure AI’s responsible and effective use in forensic science. The findings contribute to the ongoing discourse on AI’s role in forensic advancements, underscoring its potential to redefine crime-solving strategies while maintaining justice and integrity.