Solar flares, as one of the most prominent manifestations of solar activity, have a profound impact on both the Earth's space environment and human activities. As a result, accurate solar flare prediction has emerged as a central topic in space weather research. In recent years, substantial progress has been made in the field of solar flare forecasting, driven by the rapid advancements in space observation technology and the continuous improvement of data processing capabilities. This paper presents a comprehensive review of the current state of research in this area, with a particular focus on tracing the evolution of data-driven approaches -- which have progressed from early statistical learning techniques to more sophisticated machine learning and deep learning paradigms, and most recently, to the emergence of Multimodal Large Models (MLMs). Furthermore, this study examines the realistic performance of existing flare forecasting platforms, elucidating their limitations in operational space weather applications and thereby offering a practical reference for future advancements in technological optimization and system design.
@article{arxiv.2511.20465,
title = {Advances and Challenges in Solar Flare Prediction: A Review},
author = {Mingfu Shao and Suo Liu and Haiqing Xu and Peng Jia and Hui Wang and Liyue Tong and Yang Bai and Chen Yang and Yuyang Li and Nan Li and Jiaben Lin},
journal= {arXiv preprint arXiv:2511.20465},
year = {2026}
}