This research investigates the application of Artificial Intelligence of Everything (AIoE) in neuromarketing to decode consumer behavior by analyzing neurophysiological and biometric signals. The study uses EEG, GSR, and eye-tracking data to examine the cognitive and emotional responses to digital marketing stimuli. MATLAB-based machine learning techniques, including multiple linear regression and K-means clustering, are employed to identify patterns and predict purchase intent. The findings indicate a significant relationship between neural activity, emotional arousal, visual engagement, and purchase decisions. EEG alpha and beta activity, GSR responses, and fixation duration demonstrate high predictive power, with the model achieving an R2 of 0.803. The results underscore the potential of AIoE-powered neuromarketing in optimizing marketing strategies through real-time, data-driven insights. The study highlights the transformative role of neuromarketing technologies while addressing ethical considerations related to neurodata privacy.