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Marriage is one of the most important social institutions, as it greatly influences the stability of families, population, and socio-economic well-being. The changing trends of marriages and divorces in South Africa confirm the importance of predictive analysis on the stability of marriages. Marriage duration prediction is a challenging task, as it is affected by a number of factors. Recent developments in machine learning techniques provide new opportunities to model this problem. This experiment used the following seven different classifications: Random Forest, Gradient Boosting, XGBoost, Logistic Regression, Support Vector Machine, K Nearest Neighbours, and Decision Tree, which aimed to forecast the categories of marriage length (short, medium, and long). The models used the following metrics: accuracy, precision, recall, F1-score, macro ROC-AUC, and the ROC curve. The results showed that ensemble-based approaches performed better than conventional classifiers. Gradient Boosting had the best performance in macro ROC AUC and was best at discriminating classes, especially for long and medium-length marriages. Random Forest had the best overall accuracy and was best at balancing classes. XGBoost had a similar performance to that of Random Forest in predicting. SVM had a high precision value but a lower recall value. Decision Trees and KNN had poor performance. Gradient Boosting was found to be the best-performing method for multi-class prediction of duration of marriage and achieved a balance between sensitivity and specificity. The results obtained from these studies prove that ensemble learning methods can be effectively utilized for modelling complex societal events.