The rapid evolution of malware, including polymorphic and fileless variants, has weakened traditional detection methods. This paper looks at sophisticated malware detection frameworks that use deep learning and machine learning to assess important malware features such network anomalies, opcode sequences, and API requests. Signature-based techniques are effective at identifying known dangers, but they are not very effective at thwarting zero-day assaults. While they offer improvements, alternative strategies including behavior-based, cloud-based, and deep learning techniques also have drawbacks. The current detection frameworks unify real-time threat information with two IDS detection approaches to enhance security capabilities. The review extends its analysis to model interpretability and evaluates the computational burden. The assessment of experimental findings helps researchers enhance adaptable malware security through the display of improved detection precision and resilient capabilities versus evolving cyber threats.