In pediatrics, infectious disease is a subspecialty that addresses the diagnosis, prevention, and treatment of infections in children aged from birth to 21 years of age. Given their developing immune systems and frequent exposure, children are especially vulnerable to infections. Globally, infectious diseases have a significant impact, causing millions of deaths each year. The advancement of emerging technologies, such as machine learning, has gained new momentum to fight against pediatric infectious diseases. This study investigates the application of machine learning (ML) in enhancing the diagnosis and treatment of pediatric infectious diseases, aiming to improve healthcare outcomes for the pediatric population. We employed a quantitative research design approach, combining an experimental phase to develop and fine-tune ML models like KNN, NB, SVM, LR, RF, and XGBoost with a survey method to evaluate the effectiveness of the machine learning integrated software framework. We emphasize meticulous data preprocessing, utilizing the K-Nearest Neighbors (KNN) imputation method for handling missing data and the Synthetic Minority Oversampling Technique (SMOTE) for addressing data imbalances. These preprocessing steps are critical for enhancing model performance and accuracy in complex medical applications. Additionally, z-score normalization is applied to standardize datasets, ensuring stable and reliable ML model outcomes. After conducting the experiments, we found that Random Forest performed best and integrated it into a framework designed for practical use in pediatric healthcare settings. This work integrates SHAP (SHapley Additive exPlanations) into a random forest model to enhance transparency and build trust among healthcare stakeholders. A software framework incorporating these explainable models was developed to improve both usability, understandability, and transparency. We performed a usability test with clinicians, resulting in a SUS score of 74.25, which corresponds to C and acceptable on the grade and acceptability scale, respectively. Using a random forest model, we achieved 0.97 of accuracy in predicting pediatric infectious diseases, employing a 90/10 traintest split, 5-fold cross-validation, and grid search hyper parameter optimization technique. With this result integrated with the framework, our study contributes to facilitating the analysis of patient data and identification of healthcare trends, which supports the clinicians, limited in number and unable to perform a good deal of diagnoses in a short period of time. Keywords: pediatric infectious diseases, machine learning, explainability, software framework, system usability scale.