The addition of artificial intelligence (AI) to forensic science is a big step forward in finding and investigating crimes. This review paper looks at the many ways AI can be used to improve forensic methods, with a focus on how it has the ability to completely change the field. Even though traditional investigative methods work, they aren't always accurate, efficient, or scalable. AI technologies, like deep learning, machine learning, and natural language processing (NLP), can help solve these problems in new ways. Image and video analysis, pattern recognition, data mining, prediction analytics, and digital forensics are some of the most important ways AI is used in forensics. For example, AI-powered face recognition can look at huge amounts of surveillance footage with a level of accuracy that has never been seen before. Machine learning algorithms improve the analysis of fingerprint and blood spatter patterns. NLP lets you look at written documents and spoken language, which helps you figure out who is speaking and rate threats. Predictive analytics helps figure out patterns of crime and make profiles of areas, which gives law enforcement agencies useful information. AI's ability to quickly and correctly process large amounts of data cuts down on mistakes made by humans and makes forensic investigations more efficient. Adding AI to investigations, on the other hand, is not without problems. To make sure that AI technologies are used responsibly, we need to talk about things like algorithmic flaws, data quality problems, and privacy concerns. To get the most out of AI in forensics, people from different fields must also work together and get specialised training. In the future, new technologies and ongoing study should help AI-driven forensic science make even more progress. This review shows how important it is to keep coming up with new ideas and changing things in order to get the most out of AI in crime detection and get past the problems that come with it.