The detection of somatic mutations and Copy number alterations (CNAs) in cancer cells is crucial for diagnosis and careful observation; however, existing traditional methods are both insufficient and inefficient. Examining the latest advances in Machine learning (ML) techniques, such as the Random forest algorithm, and ensemble models such as SomaticSeq, which turns out to have good mutation detection accuracy and efficiency. This method also proves capable of handling a variety of sample purity and sequencing strategies. Therefore, this method can offer good results and efficiency levels when compared to conventional approaches. Although there are still challenges, such as the need for a capable training dataset and high computational requirements, this ML model promises to make significant progress in cancer diagnosis, early detection, and personalized treatment. This review paper aims to review the ML methods that involve the detection of somatic mutations and (CNA). The results of the review showed that the ML method was promising in both implications, as proven by the minimum accuracy being above 70%.