An athlete's emotional state plays a critical role in their performance, decision-making, and ability to manage stress during competitions. Understanding these affective states can provide valuable insights into their psychological and physical condition, enabling coaches and teams to make real-time adjustments and design personalized training programs. With advancements in artificial intelligence (AI) and multimodal data analysis, it is now possible to assess emotional states through non-invasive methods by analyzing visual cues such as body language, facial expressions, and dynamic movements. By developing an AI-based system capable of recognizing athletes' affective states, this research can contribute to optimizing training, improving performance, and fostering psychological resilience.