Mobile devices are now indispensable, but their widespread use makes them prime targets for malware. Traditional signature-based methods struggle against sophisticated and zero-day threats, driving the rise of Machine Learning (ML) and Deep Learning (DL) for mobile malware detection. These techniques can generalize from data and adapt to evolving attacks. This survey reviews ML and DL approaches by categorizing studies across analysis strategies (static, dynamic, hybrid), feature extraction methods, and model types (classical ML, DL). It also summarizes commonly used datasets and highlights challenges such as data scarcity and explainability while outlining future research directions. Unlike prior surveys that focus on a single perspective, our work integrates analysis type, feature set, and model to provide a holistic view of mobile malware detection.