Inaccurate classification of the malaria burden on households hinders precise evidence required for effective malaria control through strategic interventions, early detection of risk, optimal allocation of resources, informed decision-making, and formulation of appropriate policies; thus, raising the global malaria burden to approximately 95%. Addressing this menace requires the development of a tree-based ensemble model for precise classification of malaria burden on households, which utilizes characteristic features of data associated with malaria burden on households obtained from the repository of Malaria Indicator Survey of 2015 and 2021 and the Demographic and Health Survey of 2018 respectively, covering six geopolitical zones of the selected households in urban and rural Nigeria. The model incorporates majority voting, k-fold cross-validation and grid search algorithms for optimal model tuning and was implemented in the Python programming language using relevant features of data sourced from these repositories. The model performance was evaluated with precision, recall, F1-score, and Area under Curve (AUC). The overall mean accuracy achieved by the ensemble model classifier was 90.34%, indicating a high level of predictive performance and the model’s suitability for the precise classification task, and outperforming the other base models such as XGBoost (89.61%), Gradient Boosting (93.26%), CatBoost and LightGBM (89.60%), AdaBoost (87.69%). Nevertheless, the significant result is primarily driven by the high feature importance scores of the three most influential features, namely H20 (26.8%), H25 (24.5%), and H26 (21.2%). However, this model offers a practical tool for precise classification, enabling field personnel to identify high-priority areas for intervention. This tool can serve as a valuable asset for government agencies and policymakers by facilitating evidence-based decision-making and the formulation of targeted strategies to alleviate malaria burden on households. By harnessing data-driven insights, interventions can be more precisely directed toward the most vulnerable populations, thereby supporting the eradication of the disease on households. This study would further contribute to the achievement of Sustainable Development Goal (SDG) 3: ensuring healthy lives and promoting well-being for all.