By delivering more reliable, accurate, and precise decision-making through the analysis of vast behavioral and transactional datasets, machine learning (ML) is revolutionizing credit unions and enhancing loan risk assessment and fraud detection. The current state of predictive analytics is explored in this study, with emphasis on how supervised, unsupervised, and ensemble machine learning models are changing core functions like credit scoring, fraud monitoring, and portfolio risk management. It traces the evolution from inflexible rule-based and manual processes to ML-driven frameworks that utilize anomaly detection and neural networks to capture complex, fast-evolving fraud patterns that traditional methods often fail to identify. Empirical evidence shows that ensemble and deep learning models routinely achieve ROC AUC values above 0.95, with Random Forest and Gradient Boosting reporting AUC ranges of approximately 0.91 to 0.99 and recall values up to 0.88 in highly imbalanced datasets, while XGBoost achieves F1 scores around 0.79. For loan risk assessment, models such as LightGBM and XGBoost achieve AUCs of about 0.95-0.96 and accuracy above 93 percent, outperforming traditional logistic regression models. Large training databases, standardized feature engineering, and hyperparameter tuning for reliable real-time performance are among the significant model-building strategies further highlighted in the paper. Additionally, it highlights the consequences for governance, member retention, and product customization. Future research should concentrate on continuous learning systems, data privacy, and model interpretability.