Autonomous Underwater Vehicles (AUVs) are integral to marine research but are frequently constrained by energy inefficiencies, poor communication and slow decision-making. This article proposes a new Intelligent Internet of Things (IIoT) Driven Adaptive Renewal Framework (IIARF) that combines Sparse Adaptive Modulation and Coding (SAMC) for efficient IoT communication and Neuro-Adaptive Transformer Learning (NATL) for AI-based decision-making. SAMC adaptively changes modulation and coding schemes in underwater environments, minimizing energy expenditure. NATL employs transformer networks with neuro-adaptive control to improve real-time AUV navigation and environmental adjustment. The proposed IIARF framework enhances communication efficiency, maximizes energy efficiency and increases autonomous decision-making capabilities. Simulation results illustrate that a SAMC adapts the modulation and coding schemes dynamically under different underwater situations, lowering power consumption by 58.1%. It enhances communication reliability by 15.9% while lifting the data transmission rate by 183.3%. NATL utilizes neuro-adaptive control with transformers to increase real-time processing efficiency by 28.2% and minimize system response time by 40%. The advanced navigation accuracy is increased by 14.6%. Moreover, IIARF attains a 44.6% increase in resource utilization and a 24% fault tolerance rate, guaranteeing stable AUV operations. Simulation results prove that IIARF performs better than conventional AUV-IoT systems with more energy-efficient, adaptive and intelligent underwater exploration solutions.