Public health monitoring involves community efforts to prevent disease, extend life, and promote health care, considering social, cultural, and economic influences. Surveillance process plays a crucial role in addressing health issues, with children's nutrition serving as a key indicator of community well-being. To identify the priority of intervention by the government, a map indicating the mapping of public health status is needed. Numerous previous studies have explored using machine learning models to analyze children's nutrition status. However, a definitive framework for predicting children_s nutritional status using machine learning remains uncertain, which is essential for creating a public health map. Developing and validating an enhanced machine learning framework for predictive analysis is necessary to generate this public health map. This study used various algorithms in the proposed enhanced framework, including Neural Networks, Random Forests, Decision Trees, Logistic Reg.....

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