In the context of attendance monitoring, Radio Frequency Identification (RFID) technology has emerged as a critical option, providing seamless and efficient tracking capabilities. This study aims to improve the accuracy and real-time application of RFID-based attendance systems. The primary focus is on eliminating potential issues including data discrepancies and manual errors, which are frequent in traditional attendance tracking approaches. The suggested method employs advanced algorithms and technology, making use of Machine Learning (ML) for predictive analysis. The solution intends to provide a robust and intelligent attendance monitoring framework by integrating RFID technology with machine learning techniques. By examining historical attendance trends, the ML model may predict future attendance, allowing for more proactive resource allocation and management decisions. The hardware infrastructure, which includes RFID scanners and tags, ensures accurate data acquisition while reducing the chance of errors. The project's primary goals include obtaining a considerable reduction in attendance disparities, improving system efficiency, and contributing to the larger landscape of smart attendance solutions. The seamless integration of RFID and machine learning not only streamlines the attendance tracking process, but also paves the way for future advances in predictive attendance management.