The effective allocation of physical education (PE) teachers across educational regions remains a complex decision-making challenge due to variations in socioeconomic conditions, institutional requirements, student demographics, and regional resource availability. Traditional recruitment approaches often rely on generalized evaluation criteria that fail to capture multidimensional regional differences. This study proposes a Regional Intelligence Model for Physical Education Teacher Selection and Allocation Optimization (RIM-PE) that integrates socioeconomic intelligence, human resource recommendation mechanisms, and data-driven decision optimization principles. The proposed model combines regional feature analysis, intelligent candidate matching, and allocation optimization to improve fairness, efficiency, and adaptability in PE teacher recruitment. The framework is conceptually developed by integrating recommendation algorithms, feature engineering strategies, and intelligent decision approaches derived from recent computational studies. The model incorporates regional indicators such as educational demand, economic characteristics, institutional capacity, and teacher competency profiles to generate optimized placement recommendations. Theoretical analysis indicates that intelligent allocation mechanisms can reduce regional imbalance, enhance teacher suitability, and support evidence-based educational planning. The study further discusses the role of human–AI trust modeling and advanced feature engineering in improving decision reliability within intelligent recruitment systems (Ramamurthy et al., 2026). The proposed framework provides a foundation for future AI-assisted educational workforce planning and scalable teacher management systems.