Machine Learning for Predictive Analytics: A Systematic Review of Applications, Algorithms, and Challenges

Abstract

Machine learning has become a central approach to predictive analytics, enabling organizations to extract patterns from complex datasets and generate predictions that support decision-making across diverse domains. This systematic review examines the applications, algorithms, benefits, and challenges associated with machine learning for predictive analytics. The review synthesizes evidence from relevant scholarly studies addressing the use of supervised, unsupervised, and deep learning techniques in predictive tasks across healthcare, finance, education, business, manufacturing, cybersecurity, transportation, and other application areas. The findings indicate that commonly applied algorithms include linear and logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, gradient boosting methods, artificial neural networks, and deep learning architectures. Ensemble and deep learning approaches generally demonstrate strong predictive capabilities when applied to high-dimensional and nonlinear datasets, while traditional statistical and machine learning models remain valuable because of their relative interpretability, computational efficiency, and suitability for structured data. The review further identifies important benefits, including improved forecasting accuracy, early risk detection, automated decision support, operational optimization, and personalized services. However, the adoption of machine learning for predictive analytics is constrained by challenges involving data quality, limited or imbalanced datasets, overfitting, algorithmic bias, interpretability, privacy and security, computational requirements, model drift, and difficulties in transferring models across contexts. The review concludes that effective predictive analytics requires not only algorithmic performance but also robust data governance, transparent model development, appropriate validation, ethical safeguards, and continuous monitoring. Future research should prioritize explainable, fair, privacy-preserving, and domain-adaptive machine learning approaches capable of producing reliable predictions in increasingly dynamic real-world environments.