Artificial Intelligence (AI) techniques like Machine Learning (ML) and Deep Learning (DL) have become pivotal in the discipline of cybersecurity because of the steadily increasing frequency of intrusions by attackers. These cutting-edge technologies perform well for identifying and preventing cyberattacks, which may otherwise seriously harm people, businesses, and even entire nations. Machine and deep learning techniques use statistical techniques to find out patterns and outliers from large volumes of datasets. Also, deep learning and machine learning's advanced characteristics allow for the early identification of risks. This study focuses on identifying different techniques of malware detection with distinct features, datasets, methodologies, etc. used in identifying threats in mobile phones. Additionally, an approach for malware detection is provided based on a thorough examination of literature. This approach focuses on malware detection using techniques like API Tracking, Permission Tracking, and system call analyzing to differentiate malware from other genuine applications. These techniques are proposed based on thorough study and inspection of the literature available in this field. These conclusions and suggestions are intended to support researchers in their field of study.