Students with disabilities find learning challenging in traditional learning environments due to the lack of instructional strategies that are not personalized or adaptive. Various studies have focused on identifying learning difficulties such as dyslexia, dysgraphia, and dyscalculia, which require a multi-step screening process under the supervision of psychologists. Identifying these difficulties is challenging but essential, as it impacts a student's learning and academic success. Everyone's comprehension ability depends on several factors, including the experience and knowledge they bring to the learning environment. With the evolution of technology and the advancement of e-learning platforms, adaptive e-learning has bridged the gap between students' needs and educational institutions' extra classes, enabling students to select targeted courses aligned with their interests. Since the onset of COVID-19, universities have recognized the necessity of online learning and have continued to use these platforms for student assessment. Educational institutions seek innovative strategies to enhance personalized learning (PL) for students with disabilities. The use of technology in schools has created new opportunities for PL, especially for students with disabilities. Traditional learning systems frequently neglect the diverse focuses and distinct requirements of students with visual, cognitive, motor, or auditory impairments. Because of this lack of flexibility, students with disabilities are less likely to be engaged and to complete tasks. Identifying and using effective learning strategies that work for each student remains a significant challenge in education. To address this issue, this paper proposes a reinforcement learning–based PL system for students with disabilities (PLS-SD) using Q-learning. We suggest PL actions, including audio instructions, augmented reality, and text-based resources. A reward system based on real-world outcomes, i.e., how well students complete their work and how engaged they are, helps them learn. Experimental results demonstrate that the proposed approach effectively identifies optimal actions for different learner states, with immersive and adaptive strategies, such as augmented reality and interactive content, consistently achieving higher rewards. The model shows stable learning behavior across training episodes and successfully adapts its policy to maximize learner engagement and task completion. As indicated by comparing the results with currently advanced models, the proposed method outperforms approaches that are considered traditional by providing context-aware and adaptable recommendations. These findings highlight the potential of reinforcement learning to support scalable and personalized educational solutions for diverse learners.