The recommended strategy helps UAV-IoT systems schedule activities to improve QoS, use less energy, and accomplish missions quicker. A genetic algorithm creates first employment plans. It develops schedules using selection, crossover, and variation to balance energy and time. Next, a Gaussian distribution-based multi-objective optimization strategy is used to identify new fitness assessment regions. A comprehensive fitness evaluation and convergence proof provide optimal scheduling and resource management at the conclusion. This technique was compared to the genetic algorithm and multi-objective optimization with Gaussian distributions for psychological marker identification and athlete monitoring. The recommended strategy consistently outperforms others in accuracy, dependability, efficacy, and sensitivity. The recommended approach has 95 accuracy, 92 sensitivity, and 90 dependability, indicating its effectiveness in detecting psychological indications and improving athletic performance. The solution is reliable and effective, making it a better way to plan tasks and monitor sports performance in UAV-IoT systems.