Endocrinology presents a fundamental yet complex challenge within undergraduate medical education. Its intricate hormonal feedback systems and restricted clinical exposure make it difficult to master. AI presents innovative educational approaches that could overcome these challenges by utilizing adaptive learning, simulations, and immediate feedback. The purpose of this research was to evaluate how well incorporating AI-powered educational resources into undergraduate endocrinology courses impacts student learning, clinical decision-making abilities, and their level of engagement in the subject matter. This prospective randomized controlled trial (parallel-group design), conducted over a six-week endocrinology course, included 124 medical students in their third and fourth years. The students were randomly assigned to one of two groups: a group receiving instruction enhanced by artificial intelligence (AI) (n=62) and a group receiving traditional teaching methods (n=62). The AI-enhanced group benefited from adaptive learning tools, virtual patient scenarios, and an AI-powered tutoring system, in addition to regular curriculum content. The effectiveness of the intervention was assessed through the administration of pre- and post-intervention multiple-choice exams to measure knowledge acquisition, case-based clinical reasoning evaluations, and structured surveys to gauge student satisfaction. Initial knowledge levels were similar across both groups (p > 0.05). Students who experienced AI-powered teaching methods demonstrated substantially improved engagement and satisfaction levels. Incorporating artificial intelligence into undergraduate endocrinology courses substantially enhanced students' understanding of the subject matter, their ability to apply clinical reasoning, and their overall engagement in learning. AI tools prove to be valuable supplements to conventional teaching approaches and hold the potential to revolutionize medical education, particularly in intricate clinical fields.