Proactive Behavioral AI for Autism and Workforce Integration: A Multi-Layered Framework with Explainable IoT, Edge Intelligence and Crisis Response

Behavioral health, autism treatment, and clinical workforce planning have become more and more centered on Artificial Intelligence (AI). However, current methods tend to focus on performance at the cost of explainability and real-time response. This paper presents a multi-layered AI model to integrate IoT-enabled autism monitoring, workforce planning, and anomaly detection blocks derived out of the fraud analytics. The architecture makes use of edge intelligence for low-latency processing and incorporates explainability dashboards to build trust among clinicians. An emulated assessment of autism IoT, workforce scheduling, and fraud data set performance has shown accuracy improvements of 11, latency reduction of 60 and a 16-point usability trust score improvement over centralized cloud-only models. Through the convergence of edge-AI and transparency artifacts, this paper will build scalable, fair, and clinically-important AI in behavioral escalation prevention and workforce optimization.