Risk assessment in life insurance is traditionally conducted using manual techniques, which, despite their effectiveness, are labor-intensive, time-consuming, and susceptible to inconsistencies. This study presents a machine-learning pipeline that streamlines predictive modeling and handles challenges such as imbalanced data and an abundance of features by applying methods that balance data representation and simplify the dataset. Five different classifiers, including Logistic Regression as a baseline, XGBoost, CatBoost, LightGBM, and TabNet, are tuned using the grid search method and evaluated through cross-validation with metrics such as Accuracy, AUC, Recall, Precision, F1 score, and Cohen’s Kappa. The results demonstrate that the ensemble method XGBoost achieved superior performance across four out of six key metrics. Accuracy 0.5801, AUC 0.8678, F1-score 0.5070, and Cohen’s Kappa 0.4622, outperforming traditional, deep learning techniques and prior benchmarks, while TabNet exhibited the highest recall 0.5250, and CatBoost the highest precision 0.5592. This study demonstrates the potential of machine-learning techniques to offer a more efficient and reliable alternative to traditional risk estimation methods.