Dynamic loads on engineering structures are often difficult to measure directly. Therefore, indirect identification methods based on dynamic responses are commonly used. However, this approach is often influenced by ill-conditioned matrices, noise interference, unknown structural and/or material parameters, and difficulty of constructing transfer functions when the traditional physics-based model is used. To address these issues, significant strides have been made in data-driven identification of dynamic loads, which are model-free and independent of structural characteristics. This paper tries to present a comprehensive review of dynamic load identification methods based on data-driven techniques, covering two aspects: load localization and load reconstruction. Features of the widely used data-driven techniques such as the geometric method, reference database method, machine learning methods including SVM-based methods and ANN-based methods and deep learning methods are discussed in detail. Additionally, this paper offers insight into the challenges and prospects of the data-driven techniques for dynamic load identification. The review aims to provide valuable insights for identifying dynamic loads in complex structures based on data-driven techniques and suggests future research directions.