<p>This paper introduces a novel Reinforcement Learning-Based Hybrid Validation Protocol (RL-CC) that revolutionizes conflict resolution for time-sensitive IoT transactions through adaptive edge-cloud coordination. Efficient transaction management in sensor-based systems is crucial for maintaining data integrity and ensuring timely execution within the constraints of temporal validity. Our key innovation lies in dynamically learning optimal scheduling policies that minimize transaction aborts while maximizing throughput under varying workload conditions. The protocol consists of two validation phases: an edge validation phase, where transactions undergo preliminary conflict detection and prioritization based on their temporal constraints, and a cloud validation phase, where a final conflict resolution mechanism ensures transactional correctness on a global scale. The RL-based mechanism continuously adapts decision-making by learning from system states, prioritizing transactions, and dynamically resolving conflicts using a reward function that accounts for key performance parameters, including the number of conflicting transactions, cost of aborting transactions, temporal validity constraints, and system resource utilization. Experimental results demonstrate that our RL-CC protocol achieves a 90% reduction in transaction abort rates (5% vs. 45% for 2PL), 3x higher throughput (300 TPS vs. 100 TPS), and 70% lower latency compared to traditional concurrency control methods. The proposed RL-CC protocol significantly reduces transaction abort rates, enhances concurrency management, and improves the efficiency of sensor data processing by ensuring that transactions are executed within their temporal validity window. The results suggest that the RL-based approach offers a scalable and adaptive solution for sensor-based applications requiring high-concurrency transaction processing, such as Internet of Things (IoT) networks, real-time monitoring systems, and cyber-physical infrastructures.</p>