Introduction: Love addiction, a negative emotional construct, can significantly impact an individual’s personal and social life. It is characterized by emotional dependency involving both positive and negative emotions, specific interpersonal needs, and elements of self-hate. In recent years, machine learning has become increasingly valuable in predicting and detecting negative emotional states in psychology. These algorithms assist in unraveling the complexity of such phenomena. This study explores the application of 12 selected machine learning algorithms to explain love addiction among Iranian students based on these psychological factors. Method: This study utilized a convenience sample of 428 Iranian students who participated in 2024. Data collection tools included demographic questionnaires and assessments of positive and negative affect, interpersonal needs, and self-hate. The dataset was analyzed using various machine learning algorithms: AdaBoost, CatBoost, decision trees (DT), Extra Trees, k-nearest neighbors (KNN), LightGBM (LGBM), logistic regression (LogReg), multilayer perceptron (MLP), naive Bayes (NB), random forest (RF), support vector machine (SVM), and XGBoost (XGB). The input features consisted of positive affect (PA), negative affect, interpersonal needs, and self-hate, while the target variable classified love addiction into low and high levels. Results: The results showed that the random forest classifier achieved the highest performance with a mean accuracy of 0.82, sensitivity of 0.93, and an AUC value of 0.92. Other models, such as SVM and decision tree performed well with SVM achieving the highest sensitivity (0.98) but lower specificity. Feature importance analysis revealed that positive affect (PA), thwarted belongingness (TB), and interpersonal needs (INT) were the most important predictors of love addiction. Conclusion: Among the 12 machine learning algorithms, random forest demonstrated the best overall performance in predicting love addiction with superior discriminatory power (AUC = 0.92). The feature importance and the Shapley Additive Explanations (SHAP) value analyses further identified key psychological factors, such as PA and TB, which contribute to love addiction. This study highlights the potential of machine learning models in understanding and predicting psychological phenomena like love addiction, providing valuable insights for mental health professionals and researchers.