As the popularity of android smartphone operating system growing now a days. In the Android platform, the Google play store contains millions of android mobile applications those are downloaded by user for multiple purpose. Mobile apps built on Android have launched and it contains some malicious and malware attacks. So, users become a target of unethical intrusions due to open-source platform. In this work, malware with changing properties cannot be detected or predicted using typical malware detection methods. Machine learning classification techniques have been employed for many years to address these problems, and it has been found that ensemble learning produces the greatest results when it comes to identifying malware for Android devices. The ensemble learning method employs multiple learning algorithms to improve predictions, resulting in improved prediction performance. It assists with further developing AI results by combining a few models. In order to build the classification model, one must focus on both static and dynamic (hybrid analysis) aspects of the code while maintaining a balance between the learnt model’s accuracy and processing time. In this work, machine learning algorithms like K-Nearest Neighbor and Support Vector Machine (SVM) are used, along with ensemble learning techniques including Random Forest, Extra Tree Classifier, and Voting Classifier. The model’s performance is assessed using F1 score, accuracy, recall, and precision.