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Background: Competitive esports represents a rapidly developing digital performance environment in which players must regulate attention, affect, psychophysiological activation, and performance under dynamic, high-demand conditions. Although cognitive overload and related states are increasingly relevant to esports research, these constructs have not been consistently defined, operationalized, or measured. Objective: This scoping review aimed to map empirical evidence on cognitive overload and related states in competitive esports, with emphasis on cognitive demands, psychophysiological and neurophysiological markers, coping and recovery approaches, game genres, and performance outcomes. Methods: The review followed Joanna Briggs Institute guidance and PRISMA-ScR reporting principles. Searches were conducted in Scopus, PubMed, Web of Science, and EBSCOhost on 5 May 2026. Eligible studies were peer-reviewed empirical articles involving ranked, collegiate, semi-professional, professional, elite, or otherwise competitive esports players and included at least one objective or semi-objective indicator relevant to overload-related states. Results: Forty-eight studies were included. Evidence was distributed across in-game performance, stress and arousal, cardiometabolic load, mental workload, sleep and recovery, perceptual-cognitive processing, neurophysiological markers, and intervention-oriented approaches. Heart rate (HR), heart rate variability (HRV), eye-tracking, sleep or wearable measures, electroencephalography (EEG), event-related potential (ERP), cortisol, pupil diameter, electrodermal activity (EDA), cognitive tasks, and in-game metrics were used across the literature. However, these measures were rarely integrated into unified, multimodal models. Conclusions: Cognitive overload in esports is best understood as a dynamic, multidimensional state emerging from interactions between game demands, competitive context, player regulation, and performance consequences. Future research should develop genre-sensitive, multimodal, and time-synchronized models that distinguish workload, stress, fatigue, arousal, tilt, and performance decline.