Ultra-high-frequency radio frequency identification (UHF-RFID) technology provides a promising and cost-effective solution for tracking and positioning applications. However, its performance is often affected by signal attenuation, environmental variability, and radio interference. Among the influencing factors, transmission power plays a critical role in the successful detection of RFID tags. Insufficient transmission power can lead to missed reads, while excessive power may result in signal collisions or interference with nearby systems, compromising system reliability and scalability. To address the challenges of the successful detection of RFID tags, this paper proposes a custom designed UHF reader for detection framework to solve (i) featuring circular polarization to enhance the flexibility of tag orientation and reduce polarization mismatch; (ii) by integrating machine learning (ML), a system can autonomously classify environmental conditions and adjust power levels accordingly. This paper also proposes a ML-supported UHF-RFID detection framework that improves the accuracy of tag classification and automates the adjustment of transmission power in various indoor and outdoor scenarios. By leveraging the RSSI dataset and introducing an extended dataset enriched with new features and conditions, we employ ML to optimize power consumption and improve detection efficiency. Furthermore, to ensure confidentiality during transmission for collected data, encrypt and decrypt CSV (Comma-Separated Values) file are proposed. Lastly, a list of performance analysis for recommendation based on supervised learning, unsupervised learning, deep learnings are considered to solve model and device selection in RFID environments, clustering in RFID Tag, and recommended usage for indoor and outdoor environments, respectively. These recommendations serve as practical guidelines for deploying RFID-based classification systems with improved accuracy and environmental adaptability. Our experimental confirm attendance rates are notably high, with median values consistently ranging between 80% and 95%, reflecting a strong level of student engagement and participation.