Cervical cancer screening demands methods that are both scalable and temporally aware. We present a hybrid framework that couples an agent-based layer with a Hidden Markov Model (HMM) for state transitions across two follow-up windows (S1: 0 \(\rightarrow \) 6 months; S2: 6 \(\rightarrow \) 12 months) and a neural network that produces calibrated state probabilities and 6-month forecasts for auxiliary variables. The agents constrain plausible moves among diagnostic categories; the HMM formalizes persistence, progression, and regression, and links latent states to observations; finally, the neural network synthesizes these signals into risk-ready predictions aligned with routine follow-up. On test data, state-classification accuracy reached 0.979 in S1 and 1.000 in S2, with ROC–AUC \(\approx 1\) in both windows. Furthermore, not only does the predicted correlation structure closely match that of the observed data, but the odds-ratio analyses are also clinically coherent. These findings show that integrating the Agent+HMM structure with neural predictors yields stable, temporally consistent, and clinically coherent performance, offering a practical path toward risk-based decision support in cervical screening workflows.