Malware of different families often share specific behavioral patterns that can be studied and identified through Machine learning’s static and dynamic analysis. Static analysis involves the study of malicious files’ content without executing them. On the other hand, in dynamic analysis the behavioral aspects of malicious files are analyzed by executing tasks like function call monitoring, information flow tracking, and dynamic binary instrumentation. Through machine learning the static and dynamic artefacts of the malware can be used to predict the evolution of modern malware structure which can then empower systems to detect more complex malware attacks that otherwise are exceedingly difficult to predict by traditional methods.