High-energy physics (HEP) experiments generate extraordinarily large and complex datasets, posing significant challenges for real-time data analysis, event reconstruction, and reliable anomaly detection. Traditional analytical techniques often struggle to scale efficiently or fully exploit the rich structure of these data. In this context, machine learning (ML) has emerged as a transformative paradigm, offering powerful tools to enhance computational efficiency, precision, and adaptability in HEP data processing pipelines. This review provides a comprehensive overview of the integration of ML techniques in HEP, with a particular focus on their role in optimizing data analysis workflows and improving experimental performance. We examine a broad spectrum of ML approaches, including supervised and unsupervised learning methods, deep learning architectures, and ensemble models, highlighting their applications in tasks such as signal–background discrimination, feature extraction, noise reduction, and anomaly detection. Special attention is given to advanced algorithms designed for real-time data processing, which are critical for trigger systems and online event selection in modern collider experiments. The effectiveness of these methods is evaluated in the context of large-scale HEP datasets, demonstrating strong performance with metrics including an accuracy of 0.9421, sensitivity of 0.9314, specificity of 0.9507, precision of 0.9458, an F1-score of 0.9386, and an area under the ROC curve (AUC) of 0.9723. By critically analyzing current ML models and their integration into established HEP data analysis frameworks, this review identifies recent advancements, ongoing challenges related to model interpretability, scalability, and robustness, and promising directions for future research. The findings underscore the pivotal role of ML in advancing data-driven discoveries in HEP and support the development of more accurate, efficient, and scalable experimental analyses.
Nema et al. (Sat,) studied this question.