Model predictions are routinely used to help in the decision-making process. For instance, in the oil and gas industry, the accumulation of solid particles, such as sand, and the formation of a bed of solids at the bottom of the pipe can be consequential. Such accumulation may decrease the efficiency of the pipeline due to the increase in the frictional pressure loss; increase the risk of pipeline damage due to erosion; or increase the possibility of pipeline corrosion damage under the bed of solids. In order to transport the solid particles in the pipe, the fluid velocity must exceed the critical velocity required for solid particle transport. Mechanistic models are used to provide a reasonable estimate for the critical velocity needed to transport the particles. However, those models are commonly applicable in their respective ranges of data fitting; and are limited by the applicability of the empirically based closure relations that are a part of such models. On the other hand, the accumulation of experimental data makes possible the application of data-driven methods for characterizing multiphase flow for a broader range of flow conditions. This paper presents a framework to predict the fluid velocity needed to transport solid particles in a pipeline via machine learning (ML) approach. In order to prepare a dataset for training ML models, the critical velocity data are collected from available sources in literature. With the purpose of decreasing the number of input parameters for ML algorithms and to make the model similar for different types of carrying fluids, a set of dimensionless variables has been used. To create the predictive models, three ML algorithms are applied: Random Forest, Support Vector Machine, and Gradient Boosting. The fine-tuned models are compared using statistical analysis to identify the ones that provide the most accurate velocity predictions for different operating conditions. Moreover, the predictive abilities of the models are further validated by comparing their performance with different mechanistic models. The proposed ML approach demonstrates high accuracy in predicting critical velocity across a wide range of flow conditions and inclination angles.