Latest research in Android Malware Analysis focuses on leveraging machine learning, deep learning, and graph-based methodologies to enhance the detection, classification, and mitigation of malware on Android devices. Studies employ techniques such as system call analysis, directed graph representations of application behavior, and two-step machine learning frameworks to improve accuracy, handle obfuscation, and categorize malware effectively. Innovative models address challenges posed by rapidly evolving mobile applications, scalability, and evasion tactics, while feature engineering and deep learning architectures enable more robust detection even in complex and obfuscated malware. These advancements collectively demonstrate the growing impact of intelligent, data-driven approaches in securing Android ecosystems and improving the efficiency and reliability of malware analysis.