This study evaluates machine learning (ML) models predicting response to induction therapy in acute myeloid leukemia (AML) patients, integrating clinical and gene expression data. After preprocessing data from the Beat AML trial, scaling, feature selection, and hyperparameter tuning were conducted. Six ML models were tested, with the XGB Classifier achieving the highest performance (AUROC = 0.86, AUPRC = 0.92). Findings highlight the potential of ML models in predicting AML treatment response and identifying relevant features.