Malware Detection and AI Integration: A Systematic Review of Current Trends and Future Directions

Over the past decade, the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks. Traditional detection techniques, while still in use, often fall short when confronted with modern threats that use advanced evasion strategies. This systematic review critically examines recent developments in malware detection, with a particular emphasis on the role of artificial intelligence (AI) and machine learning (ML) in enhancing detection capabilities. Drawing on literature published between 2019 and 2025, this study reviews 105 peer-reviewed contributions from prominent digital libraries including IEEE Xplore, SpringerLink, ScienceDirect, and ACM Digital Library. In doing so, it explores the evolution of malware, evaluates detection methods, assesses the quality and limitations of widely used datasets, and identifies key challenges facing the field. Unlike existing surveys, this work offers a structured comparison of AI-driven frameworks and provides a detailed account of emerging techniques such as hybrid detection frameworks and image-based analysis. The findings indicate that AI-based models trained on diverse, high-quality datasets consistently outperform conventional methods, particularly when supported by feature engineering, explainable AI and a multi-faceted strategy. The review concludes by outlining future research directions, including the need for standardized datasets, enhanced adversarial robustness, and the integration of privacy-preserving mechanisms in malware detection systems.