In today's technological landscape, the ubiquitous use of mobile devices underscores their critical importance in facilitating daily tasks and enabling a wide array of functionalities, from communication to commerce and entertainment. However, this widespread adoption also brings significant concerns regarding security and privacy, especially with the proliferation of mobile applications capable of accessing sensitive data without explicit user consent. The Android operating system, renowned for its openness and extensive app ecosystem, faces substantial security challenges due to its susceptibility to malware attacks. Malicious software, covertly embedded within seemingly legitimate apps, poses serious threats such as data theft, unauthorized access, and device compromise. This study focuses on using Random Forest, Extra Trees, Logistic Regression, Gradient Boosting, and Support Vector Machine algorithms to develop an Android-based platform for robust malware detection in applications. The research aims to evaluate and compare the performance of these algorithms in terms of accuracy, precision, recall, and F1-score. The results show that the Logistic Regression algorithm achieved the highest accuracy with 97.31%.  Additionally, it seeks to benchmark these results against a prior model utilizing different machine learning algorithms, aiming to identify the most effective approach for mitigating Android malware threats. By advancing detection capabilities through sophisticated machine learning methodologies, this study contributes to ongoing efforts to safeguard mobile device users from evolving cybersecurity threats.