In the rapidly evolving digital age, the sophistication and frequency of malware attacks have escalated, posing severe threats to information security. Traditional malware detection systems, largely based on signature-matching techniques, are proving inadequate against modern, dynamic malware variants which frequently update their signatures to evade detection. This review paper explores the efficacy of machine learning (ML) algorithms in identifying and classifying malware based on patterns and behaviors, offering a robust alternative to conventional methods. Each category’s potential and challenges are examined through case studies and recent research findings, highlighting the adaptability and accuracy of ML models in detecting unknown malware types by learning from historical attack patterns and behaviors. The review concludes with a discussion on future directions for research, emphasizing the necessity for more sophisticated neural network architectures and hybrid models that combine different ML techniques to improve detection rates and reduce false positives.