Advances and Challenges in Solar Flare Prediction: A Review

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 Language Models (MLLMs). Furthermore, this review compares representative solar flare forecasting systems evaluated under heterogeneous settings, ranging from offline retrospective experiments to quasi-operational and operational deployments, and clarifies the relationship between reported performance metrics and real-time forecasting scenarios.