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Expert systems, a form of artificial intelligence (AI), are typically designed to solve many real-world problems by reasoning through knowledge, which is primarily represented as <i>IF–THEN rules</i>, with the information acquired from humans or domain experts. However, to assume such rules for <i>personalized decision-making</i> in an intelligent, context-aware mobile application is a challenging issue. The reason is that different mobile users may behave differently in various day-to-day situations, i.e., not identical, and thus the rules for personalized services must be reflected according to their symmetrical or asymmetrical behavioral activities. Therefore, our key focus is to solve this issue through adding personalized decision-making <i>intelligence</i> to develop powerful mobile applications to assist the end-users. To achieve our goal, in this paper, we explore on “<i>Mobile Expert System</i>”, where we take into account <i>machine-learning rules</i> as <i>knowledge-base</i> rather than traditional handcrafted static rules. Thus, the concept of a mobile expert system enables the computing and decision-making processes more actionable and intelligent than traditional ones in the domain of mobile analytics and applications. Our experiment section shows that the context-aware machine learning rules discovered from users’ mobile phone data can contribute in building a mobile expert system to solve a particular problem, through making personalized decisions in various context-aware test cases.