Malware is a serious risk to computer systems and can result in large financial losses for businesses and end users alike. It has become a major online worry as its use spreads throughout the world, affecting industries including banking, e-commerce, and healthcare. Developing efficient detection systems requires an understanding of malware kinds, assault aims, and infection strategies. This paper examines Android malware, including how it infiltrates, how Android’s built-in security models work, and how to get around antivirus software. To counter these dangers, a variety of machine learning and deep learning techniques have been put forth; most of these rely on static features because they are less expensive to compute. This chapter examines the advantages and disadvantages of various detection methods using Deep Neural Network (DNN) experiments on various datasets, offering an organized method for designing a robust malware detection system.