Health economic evaluation that encompasses decision analytic model is a beneficial approach for assisting decision maker to choose the best health intervention for patients. Decision analytic model has been increasingly applied in health economic evaluation. This mathematical approach is mostly used for conducting cost-effectiveness of healthcare interventions. Decision tree and Markov model has been widely applied in the past 20 years. Decision tree is the simplest form of decision model that drawn by the series of branches and clear pathways. Meanwhile, Markov model is one of the powerful approaches that employ stochastic process in health economic evaluation. This paper describes the applications of those two models in tobacco cessations, specifically for pharmacological interventions. First, decision tree for cost-effectiveness of smoking cessation program with pharmacist and therapies interventions compared to no program or self-aid cessation. Second, the application of Markov model estimates cost-effectiveness of veranicline, in comparison to bupropion. Markov model is constructed with morbidity and mortality states that consists of: well/no morbidities, lung cancer, COPD, stroke, myocardial infarction, and dead. This paper provides step by step of populating and constructing the model-with some modification of data. Several sections discuss the understanding of transition probabilities, costs data, cohort simulation, and the role of sensitivity analysis. Other models, despite deterministic approach, probabilistic approach are also reviewed. Both of models had both advantages and limitation that analysts should be aware of. Translating the ‘real world’ to mathematical model yields beneficial and insightful information for analysts. In addition, it could fulfill the need of evidence-based policy by decision maker. From simulation, the model may easy to be replicated-with appropriate context to generate evidence related health and costs.