Daylighting positively impacts energy consumption, comfort, health, and performance, leading to the increasing use of fully glazed facades in office buildings. However, selecting the appropriate window glass is critical, as it affects solar radiation, heat gain/loss, and daylight quality. Advanced window glasses improve energy efficiency but may distort daylight’s color and spectrum, creating undesirable lighting conditions. The rise of LED lighting, designed to reduce energy use, introduces challenges with its blue light emissions, which can disrupt circadian rhythms. This research integrates daylight and artificial lighting to evaluate their combined effects on cognitive performance, satisfaction, attention, and alertness. Artificial Neural Networks (ANN) and fuzzy logic models were employed to identify optimal lighting conditions, considering illuminance, color temperature, spectral distribution, and glass types. Two offices were tested with ten types of window glass and dynamic LED systems. Results show that dynamic LED lighting systems significantly enhance Circadian Stimulus (CS) and Equivalent Melanopic Lux (EML), particularly in combination with certain glass types. Clear and smart glass provided the best results for task performance and user satisfaction, while photovoltaic and tinted glasses led to lower satisfaction. The effect of lighting conditions was evident in paper-based visual tasks, whereas computer-based tasks were more related to demographic information than lighting conditions. ANN models successfully predicted performance outcomes with an accuracy range of 40% to 93%. Performance classification was successfully achieved through fuzzy logic models, and the methodology of this study offers valuable guidance for future research, providing a framework that can be integrated into building performance evaluation systems.