Numerous studies have explored the association between hydro-climatic factors and health outcomes. However, few have concentrated on developing predictive models based on these climatic variables, particularly hydro-climatic factors such as temperature, precipitation, or wetness indices. This research aims to develop a predictive model utilizing statistical model techniques, like DLNM, cohort analysis and mixed logistic regression, to assess the impact of hydro-climatic variables on malaria test results while accounting for key demographic factors. The study will consider various factors as potential effect modifiers. These include demographics such as sex of the child, age in months, mother's education level, and number of household members; living conditions like insecticide-treated bed nets (ITN) use, residence type, major source of drinking water, type of toilet facility, household electricity, and mobile phone ownership; climatic factors including year, month, average monthly temperature (potential confounder), and climate zone; and socio-economic factors such as Relative Wealth Index, housing quality, and region (urban or rural). Large dataset-based statistical modeling enables a more comprehensive understanding of the complex interplay between hydro-climatic variables and malaria outcomes. By leveraging extensive data and advanced machine learning techniques, this study aims to improve predictive accuracy and provide valuable insights for targeted malaria interventions.