The expansion of Android devices in today’s digital landscape highlights the critical demand for robust security actions to counteract the escalating threat of malware. The influence of Android malware extends beyond smartphones. While Android is mainly utilized on smartphone devices presently, its reach will expand to include Internet of Things (IoT) devices. Notably, an Android-based operating system tailored for IoT, initially named “Android Things” and later rebranded as “Brillo,” has already been presented. As an outcome, Android malware will increasingly impact a broader scope of devices beyond just smartphones. This research paper aims to provide a comprehensive learning of the Android architecture, and the general landscape of malware threats and proposes a genetic algorithm-based security framework for malware detection. We employed the random forest and ANN classifiers and got 91.441% and 97.206% accuracy, respectively. We expect that this research will inspire researchers to work in this direction.