Many institutions have suffered significant financial losses as a result of malware's rapid growth over the previous decade, according to studies. Anti-malware businesses have come up with a variety of ways to protect against these threats. The anti-malware profession is facing new problems due to the increasing speed, size, and complexities of malware. During malware detection, malware classification is a key element of malware analysis. In order to determine whether a given sample is infected with malware or not, a range of analysis methods can be utilized, including static analysis, dynamic analysis, and hybrid analysis techniques. After examination, virus and benign files may be easily distinguished by their distinct properties. Detection systems are more successful when they can identify specific malware traits using analytical approaches. Static and dynamic analysis tools may be used to build up analysis settings in a variety of ways. The malware classifiers are trained in the second step. Malware categorization used to be done using conventional techniques, however nowadays machine learning algorithms are utilized since they are able to handle the increasing complexity and speed of malware evolution. Machine and deep learning approaches have advanced the field of malware detection by enabling the development of more effective and efficient techniques. This research paper provides a comprehensive examination of the current state-ofthe-art in malware detection techniques, focusing specifically on the latest advancements and approaches utilizing machine learning and deep learning methods.