Artificial intelligence-driven adaptive learning systems have transformed the landscape of contemporary education by personalizing the teaching–learning process in real time according to learners’ abilities, behaviors, and performance. Personalized learning refers to a pedagogical model that tailors instruction, content delivery, and assessment according to the learner’s pace, interest, and cognitive profile. The emergence of adaptive technologies powered by artificial intelligence (AI) algorithms such as machine learning, natural language processing, and data analytics has enabled educators and institutions to create intelligent environments that respond dynamically to each learner’s needs. This approach contrasts with traditional classroom models that offer standardized instruction, often overlooking individual diversity. AI-driven personalized learning systems analyze student data, predict learning patterns, and recommend suitable resources or interventions, thereby ensuring an inclusive, efficient, and goal-oriented educational experience. The abstract explores how adaptive technologies enhance engagement, retention, and comprehension, while promoting lifelong learning and equitable access. These systems integrate tools such as intelligent tutoring systems, recommender algorithms, and cognitive modeling frameworks that continuously adapt curricula. Beyond cognitive learning, AI technologies have begun to address affective domains by detecting student emotions and motivations through multimodal data inputs such as facial expressions, keystrokes, and speech patterns. As education shifts toward digital ecosystems, the integration of AI enhances scalability, flexibility, and personalization across both formal and informal learning contexts. The study further identifies challenges related to ethics, data privacy, bias mitigation, and the need for pedagogical alignment with AI-powered automation.