With the explosive growth of Android applications, a tremendous threat is derived by the large number of sophisticated malware. Current detection technologies are usually confined to narrow dataset coverage, depend on static analysis too much, have limited real time performance, show ineffectiveness in adapting to zero-day malware, and provide low interpretability in models. To overcome these challenges, we present an AI-Driven Intelligent Malware Detection Framework for Android Ecosystems that combines state-of-the-art machine learning methods with hybrid static-dynamic analysis techniques and explainable AI approaches. It uses ensemble learning based on graph neural networks to detect known and new malwares with a low level of false alarms. Optimization with its lightweight model enables efficient on-device/edge deployment and system is suitable for real-time detection applications in resource-poor environments. Ongoing learning functions combined with live threat intelligence feeds increase the system's ability to adapt to new malware behaviours. Furthermore, integrating explain ability to models enhances model transparency for regulatory compliant and practitioner trust. Through extensive experimental validation we show that the framework achieves better accuracy, scalability, generality, and robustness than state-of-the-art techniques, making it a pragmatic and solid step forward towards a secure Android eco-system.