Breakthroughs in neuroscience and mental health emerge from integrating ancient healing methods with modern computational models. A novel approach combining Raga Therapy with Non-Linear Computational Models provides insights into neuronal healing and emotional well-being. EEG signals, best analyzed using advanced nonlinear models like artificial neural networks (ANNs) and autoregressive (AR) models, help assess the effects of Carnatic ragas—Shankarabharanam, Hamsadhwani, and Bhairavi—on brainwave patterns linked to emotional regulation and cognitive clarity. This study applies the AR model to predict EEG signal responses during raga exposure. Thirty healthy participants underwent EEG data collection through 15-min sessions for each raga. Results showed that Shankarabharanam increased alpha band activity, linked to relaxation and emotional stability. Hamsadhwani enhanced beta band intensity, while Bhairavi induced moderate alpha-theta shifts, suggesting reduced anxiety and stress. A paired-sample t-test confirmed significant increases in post-therapy alpha band activity (M = 9.24, SD = 1.15) compared to pre-therapy levels (M = 6.87, SD = 1.04), t(49) = 7.83, p < 0.001. The AR model demonstrated that Shankarabharanam had the highest precision in predicting relaxation effects, while Hamsadhwani and Bhairavi showed potential for improving concentration and stress relief. By effectively tracking brain signal evolution, the model validates Raga Therapy’s role in neurotherapy. Further research explore additional frequency bands and nonlinear models to deepen understanding of raga-induced neural mechanisms in mental health. Integrating computational and classical methods enhances neurotherapy's potential for emotional and cognitive healing.