This study looked at how well different machine learning models could predict wound healing in people with diabetic foot ulcers. The researchers created a clear framework for how to properly use machine learning in complex medical problems. They used a dataset of 700 patients that included 199 clinical features such as age, blood pressure, oxygen levels, and wound size. After cleaning the data, they chose the most important variables using statistical tests like ANOVA and chi-square, along with advice from medical experts. They filled in missing information with an imputation method called MIDAS and balanced the dataset using adaptive synthetic sampling. Seven different models were tested, including logistic regression, support vector machine, random forest, XGBoost, and neural networks. The support vector had the highest performance, with an accuracy of 85% and a very high AUC score, showing strong predictive power. Random forest and XGBoost also performed well. The results showed that good data preparation and model calibration are key to making machine learning useful for real clinical decisions.