Due to the rise in cybercrimes in recent years, digital forensics is becoming a crucial subject to research in order to gather reliable evidence. In order to recreate events, forensic investigators encounter challenges with data collecting and processing. Machine learning enables investigators to conduct more effective and efficient investigations utilising a variety of algorithms because of the significant contact that people have on a regular basis. A subset of artificial intelligence is machine learning. It is a branch of science that focuses on creating algorithms and computer models that can carry out particular activities without programming, such as dataset training and testing, and its potential to support investigations. In the course of an investigation, digital evidence is examined and analysed using a variety of machine learning techniques, which are reviewed in this paper. Every machine learning algorithm operates on a certain Based on the characteristics, each machine learning algorithm targets a particular area of digital forensics and solves challenges like complexity, data volume, timing, correlation, consistency, etc. Additionally, this study contrasts machine learning algorithms based on accepted standards. Forensic Chemistry can be defined as the practice of application of our knowledge in the field of chemistry to solve crimes. A forensic chemist can assist in the identification of unknown materials found at a crime scene There are several methods that a chemist can adopt from chemistry to help solve uncertainties at a crime scene. Forensic chemists use a variety of instruments to identify unknown substances found at a scene. Some examples of applications of forensic chemistry Spectroscopy techniques are used to check the purity of materials.Detecting illegal drugs and narcotics using identification and separation techniques.