PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Predicting sports performance has become a central focus in sports analytics, driven by the increasing availability of data and the growing recognition of its potential impact on decision-making in the sports sector. Time series analysis and real-time prediction of athletic performance involve forecasting an athlete’s performance over time, allowing coaches and sports scientists to refine training programs, manage workload, and make informed strategic decisions. This study thoroughly examines time series prediction and real-time prediction in sports, as well as the artificial intelligence (AI) techniques employed by prior researchers. The review is conducted with precision, strictly following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. This article examines time series prediction and real-time prediction methodologies that utilize machine learning (ML) and deep learning (DL) approaches, spanning the period from 2020 to 2025. This article covers the range of AI methodologies from the most basic to the most advanced models. A detailed assessment of ML and DL methodologies, grounded in prior research findings, is presented. All approaches examined in this paper significantly influence the primary future study, which focuses on the hybrid long short-term memory (LSTM) peephole integration with gated recurrent unit (GRU) for use in track cycling sports, the principal objective of the research. This research is consistent with the United Nations’ Sustainable Development Goals.