This study presents a fuzzy logic-based framework to enhance emotional intelligence (EI) in AI systems, aiming to improve the quality of human-AI collaboration. Traditional AI systems, while proficient in logical decision-making, often fail to interpret and respond to human emotions appropriately. To address this gap, the proposed model integrates fuzzy logic to manage the ambiguity and complexity inherent in emotional states. Key input variables include Human Emotional State (HES), AI Emotional Intelligence Level (AEIL), and Interaction Context (IC), which together determine the AI Response Appropriateness (ARA). Through the design of tailored membership functions and fuzzy inference rules, the system adapts its responses based on the emotional context and human-AI dynamics. Simulations and visualizations, including a 3D surface viewer, demonstrate the model’s capacity to produce emotionally appropriate responses in real-time scenarios. Compared to traditional models, this fuzzy logic approach shows superior adaptability and empathy, suggesting significant potential for emotionally intelligent AI in fields such as healthcare, education, and customer service. Future research will explore additional emotional indicators to further refine the system’s accuracy and responsiveness.