This study focuses on the development of a multidimensional adaptive e-learning system based on feedback mechanisms and data-driven decision-making. The proposed system aims to improve the learning experience by continuously analyzing student performance and adjusting educational content accordingly new data driven technologies [1].To achieve this, we employed a combination of data collection, processing, and analysis methods. The system gathers information from both primary and secondary sources. Primary data is obtained directly from students through surveys, tracking their demographic characteristics, learning preferences, and academic performance. These data points are used to build individualized learning profiles and identify deviations from expected progress [2]. The system also records user interactions, such as task completion rates, number of attempts, and error types, allowing for detailed behavioral analysis. Secondary data, including findings from authoritative research on adaptive learning, were used to refine the system’s decision-making algorithms.Machine learning techniques play a crucial role in data processing. We utilized the k-means clustering algorithm to categorize students [3] based on similar learning behaviors, such as average scores and time spent on tasks.