<p>In traffic flow prediction tasks, spatio-temporal dependencies are the main features, while various external factors from different sources also affect traffic fluctuation. This paper reviews research on traffic flow prediction based on multi-source data fusion over the period from 2020 to 2025, aiming to discuss multi-source data and attributes used for traffic flow prediction. We also review deep learning models for multi-source traffic flow prediction, which can extract spatial and temporal dependencies better. In addition, we classify the spatio-temporal and external data fusion methods into different modes and analyze the relative performance of state-of-the-art (SOTA) methods accordingly. On public datasets such as PeMS, METR-LA, and PeMS-BAY, attention mechanisms, which can adaptively quantify and enhance spatio-temporal features most informative for prediction results, when combined with Graph Neural Networks (GNN) and Temporal Convolutional Networks (TCN), yield models with considerable advantages. Additionally, models integrated with Large Language Models (LLM) have achieved significant results in long-sequence prediction and multi-source contextual information processing. Evaluations across multiple datasets show that models adopting the joint fusion mode of spatio-temporal features outperform methods that extract temporal and spatial features independently. Specifically, key challenges include addressing large-scale data missing, the scarcity of comprehensive multi-source benchmark datasets, limitations in long-term prediction, and the need for adaptive model updates to keep up with evolving road networks. Future directions focus on integrating knowledge graphs to structure heterogeneous data, optimizing LLMs through regional fine-tuning and incremental learning, and developing efficient multimodal models capable of handling diverse data types.</p>