Adaptive learning systems are data-driven educational technologies that personalize instruction in real time based on individual learner performance, behavior, and needs. This systematic literature review examines empirical studies published between 2020 and 2025 that explore the implementation, effectiveness, and challenges of ALS in higher education. A total of sixteen studies were analyzed using thematic synthesis to identify key methodological trends, outcomes, and emerging themes. The findings reveal a strong preference for mixed-methods approaches, combining system usage data with surveys, interviews, and performance metrics to capture both measurable impacts and user experiences. Most studies reported positive outcomes, including improved student engagement, academic performance, and personalized learning experiences. AI integration, real-time feedback, and gamification were identified as key enablers of ALS success. However, challenges such as data privacy concerns, high implementation costs, and technical limitations were also commonly reported. This literature review highlights a clear trend toward scalable, learner-centered ALS solutions in academia and identifies the need for robust evaluation frameworks and ethical governance. By mapping methodological patterns and key findings, the study offers valuable guidance for educators, policymakers, and researchers aiming to integrate adaptive learning systems into higher education. Ultimately, this work contributes to the broader discourse on educational innovation by evaluating how adaptive learning technologies can support inclusive, effective, and data-informed academic practices.