Humanoid robots equipped with emotion-sensitive artificial intelligence (AI) are set to redefine human–robot interactions by enabling robots to understand, adapt, and respond to emotional cues in real time. This research introduces a neuromorphic AI-driven electronic skin (e-skin) designed to mimic human somatosensory functions, enhancing the social cognition of humanoid robots. Traditional tactile sensors in robotics primarily focus on force and pressure detection, lacking the ability to interpret nuanced emotional signals. To bridge this gap, we propose a novel multimodal e-skin architecture integrating flexible high-resolution pressure sensors, temperature sensors, and electrostatic detectors. These sensors capture complex tactile feedback, which is processed using neuromorphic computing models, specifically spiking neural networks (SNNs), a bio-inspired AI model optimized for real-time sensory data interpretation. Unlike conventional deep learning models, SNNs offer low-power computation, rapid adaptation, and real-time emotion recognition, making them ideal for embedded robotic systems. The AI-driven e-skin enhances context-aware decision making, allowing humanoid robots to distinguish between stress, comfort, and affection through touch patterns and environmental stimuli. To ensure fast response times and energy efficiency, the system incorporates edge AI processing, reducing reliance on cloud-based computation while maintaining low-latency interaction. The proposed framework is validated through experimental tests, demonstrating enhanced emotion perception accuracy and adaptability in human–robot collaboration, healthcare assistance, and interactive robotics. By integrating neuromorphic AI with advanced tactile sensing, this study paves the way for the next generation of socially intelligent humanoid robots, fostering a seamless blend between artificial intelligence and human emotional intelligence.