The rise of malware attacks on Android devices necessitates robust and efficient detection mechanisms to protect users’ security and data integrity. This study proposed machine-learning techniques to detect malware on Android devices. By analyzing various features and behaviors of known Android malware samples, we train the machine-learning models to identify instances of malware accurately. To assess the performance of our method, we conduct experiments using a dataset of Android malware samples and evaluate the efficiency of the ML using metrics, like accuracy, precision, recall and F1 score, execution time, and memory usage. Our findings demonstrate that various machine-learning approaches accurately detect Android device malware. Furthermore, exploring today’s changing landscape of safeguarding Android devices from malicious attacks remains crucial. The research aims to contribute to the ongoing discourse on improving the security of Android systems amidst the changing world of malware risks. We harness the capabilities of machine learning to establish a robust malware detection system for detecting malware on Android devices. Our research confirms that ensemble methods like Random Forest and Bagging Classifier provide the best accuracy and efficiency in malware detection and are well suited for practical use in real-world scenarios.