Thailand has set a goal of eliminating malaria by 2024 in its national strategic plan. In this study, we used the Thailand malaria surveillance database to develop hierarchical spatiotemporal models to analyze retrospective patterns and predictPlasmodium falciparumandPlasmodium vivaxmalaria incidences at the provincial level. We first describe the available data, explain the hierarchical spatiotemporal framework underlying the analysis, and then display the results of fitting various space–time formulations to the malaria data with the different model selection metrics. The Bayesian model selection process assessed the sensitivity of different specifications to obtain the optimal models. To assess whether malaria could be eliminated by 2024 per Thailand’s National Malaria Elimination Strategy, 2017–2026, we used the best-fitted model to project the estimated cases for 2022–2028. The study results based on the models revealed different predicted estimates between both species. The model forP. falciparumsuggested that zeroP. falciparumcases might be possible by 2024, in contrast to the model forP. vivax, wherein zeroP. vivaxcases might not be reached. Innovative approaches in theP. vivax-specific control and elimination plans must be implemented to reach zeroP. vivaxand consequently declare Thailand as a malaria-free country.