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This study proposes a set of AI-driven frameworks for conducting visual modeling and probabilistic simulation of strategic pathways in cross-strait unification scenarios focusing on the Taiwan Strait. The model incorporates Bayesian networks to capture causal dependencies among variables such as economic integration, political reconfiguration, military deterrence, and international intervention; employs Markov chains to simulate the evolution of long-term social opinion in Taiwan; and evaluates optimal unification strategies under multiple scenarios via a decision tree algorithm. The framework also integrates system dynamics and advanced visualization tools to enhance interpretability and decision support. Simulation results show that economic dependence is the core driver for peaceful unification: when Taiwan’s economic dependence on China exceeds 60%, the probability of peaceful unification rises to over 70% within 20 years. Sustained military deterrence reduces the likelihood of direct external intervention, and the probability of military unification is higher if U.S. and Japanese response is limited. The study also finds that a shift in the island’s political ecology is crucial, with the dominance of pro-continental parties increasing reunification probability by more than 30%. In short-term strategies (within 5 years), success can exceed 85% if high-intensity military pressure, economic blockade, and political polarization are combined with blocking U.S. and Japanese intervention—though such strategies involve high geopolitical risks. To mitigate these, the model suggests supplementing with strategic deception, diplomatic mediation, psychological warfare, and precision military operations. This study provides a transferable modeling approach for simulating complex geopolitical scenarios, combining probabilistic insights with dynamic visualization to support strategic planning and policymaking.