<![CDATA[The escalation of post-pandemic malware threats has surpassed the capacity of conventional defenses, as social engineering techniques and code manipulation have become primary infiltration instruments exploiting vulnerabilities in user behavior and system structures. This study aims to analyze the evolution of malware propagation techniques, evaluate the effectiveness of digital forensic investigations, and test the robustness of artificial intelligence-based detection methods. Employing a Systematic Literature Review (SLR) approach toward case studies and algorithmic experiments from the 2023–2025 period, this research synthesizes data from real-world attack investigations and machine learning model performances. The results indicate that while algorithms such as Decision Trees and Ensemble Learning offer high accuracy, their effectiveness is increasingly compromised by adversarial attacks capable of deceiving AI logic. Furthermore, forensic findings in ransomware cases confirm that aggressive encryption speeds necessitate a shift in mitigation strategies from post-incident analysis to proactive hybrid defenses. This study concludes that the integration of behavioral detection technology and systemic resilience through data backup management is the primary key to countering the mutation of contemporary cyber threats.]]>
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