Explainable emotion forecasting and empathic dialogue generation via a Hybrid Markov–LLM framework

This rising prevalence of mental illness has led to the urgent requirement for forward-thinking, smart systems capable of predicting emotional states and providing context-aware, therapy-based advice. Existing emotion recognition and conversational AI focus on static sentiment analysis or real-time generative conversation but do not predict future emotional streams or provide explainable, clinically safe interventions. For overcoming the above shortcomings, the paper proposes one integrating a Hybrid Markov Chain model with an LLM-driven therapeutic reasoning engine. The hybrid Markov model combines empirical and synthetic emotion transition data to predict realistic but adaptive psychological dynamics, enabling 7-day ahead forecast of emotional drift in a number of affective states, namely, Anxiety, Depression, Stress, and Severe Distress. Three prompt architectures Hybrid - Instruction and Roleplay, Hybrid ＋ Few-shot, and Hybrid ＋ Few-shot ＋ Chain-of-Thought (CoT) were evaluated based on metrics including Empathy Quality (EQ), Forecast Awareness (FA), Reasoning Transparency (RT), Clinical Safety (CS), and Interpretability (INT). Experimental results indicate that the CoT-scoring model achieved an overall average improvement of 12.9 % in performance, almost approximating LLM responses with predicted emotion trajectories at the cost of clinical safety, which was kept above 90 %. The proposed framework enables predictive-driven, interpretable, and adaptive therapeutic dialogue, fostering early intervention, enhanced user trust, and personalized mental wellness treatment. Through the synergy of probabilistic emotional modeling and explainable LLM inference, this solution represents a new paradigm in predictive affective computing that harmonizes data-driven prediction with empathetic, explainable AI-mediated psychotherapy.