This thesis presents a systematic review of machine learning (ML) techniques for predicting phishing attacks, a prevalent form of cybercrime exploiting human vulnerabilities through deceptive communications. With the rising frequency of internet-based fraud, developing detection systems is critical. The aim of this thesis is to explore the efficacy of various ML techniques in detecting phishing attacks. This work employs the PRISMA framework to evaluate existing research on various ML models' effectiveness in identifying phishing attempts. The review highlights significant advancements in ML methodologies that enhance phishing detection, emphasizing the strengths and limitations of models such as Random Forest, Decision Trees, and Neural Networks among others. The findings are gathered from comprehensive analyses of scholarly articles, focusing on ML’s ability to adapt to the dynamic nature of cyber threats. By comparing model performances, this study not only identifies the most effective techniques.This research underscores the necessity of continuous improvement in cyber defense technologies to keep pace with evolving cyber threats. Key contributions include a detailed comparative analysis of ML models, offering a foundation for future studies to build upon and refine cybersecurity strategies.