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The progressive application of artificial intelligence technology within oil and gas exploration has resulted in an inevitable shift towards the transformation of geomechanical parameter prediction from a traditional to an intelligent approach. This paper presents a comprehensive review and critical analysis of machine learning algorithms in the direct and indirect prediction of rock mechanics parameters, pre-drilling prediction, monitoring while drilling and post-drilling evaluation of formation pore pressure, 1D in-situ stresses and 3D in-situ stresses field prediction. Furthermore, the paper compared machine learning models, input parameters, sample data volume, output parameters, and model prediction performance under different tasks. It has been demonstrated that machine learning algorithms exhibit superior performance in terms of accuracy, timeliness, and applicability in geomechanical parameter prediction compared to laboratory tests, field tests, and empirical model calculations. The current research emphasis is on hybrid models, deep learning models, and physical-constrained neural network models, which have been validated as highly accurate, robust, capable of generalization, and easily interpretable. However, the existing research primarily concerns the prediction of 1D geomechanical parameters post-drilling. Consequently, it is not possible to effectively predict 3D geomechanical parameters prior to drilling or during the drilling process. In order to facilitate the digital and intelligent transformation of geomechanical parameters, an intelligent prediction framework for geomechanical parameters is proposed in this paper. This framework considers the influence of multi-source data, including seismic, logging, and mud log data on the prediction of geomechanical parameters. The machine learning model, which is driven by data and physics, enables the prediction of 3D geomechanical parameters. This model is updated in real-time through the most recent drilling data, thus allowing for the pre-drilling prediction, monitoring while drilling and post-drilling evaluation of regional 3D geomechanical parameters. In addition, the key technical problems facing the intelligent prediction of geomechanical parameters are identified: (1) The transformation of unstructured data types should be minimized, the complexity of the data set should be reduced, and the consistency and comparability of the data should be ensured. (2) Multi-source data fusion should be conducted, and multi-source data sets, including seismic, logging, mud log, laboratory tests, and field test data, should be constructed. Subsequently, data processing and feature selection should be performed. (3) Machine learning models should be enhanced to improve performance, integrated models should be adopted to improve prediction accuracy, and mechanism models and domain knowledge should be integrated to enhance model robustness and explainability.