The clogging of porous media with solid particle suspension flow is modeled using two empirical parameters of filtration coefficient (7) and formation damage coefficient (/3). These parameters are typically determined through coreflood tests. This study employs machine learning techniques to predict 7 and /3 using experimental data from open literature. The prediction of /3 is based on critical porosity fraction (gamma) data and a power law equation relating /3 and gamma. Collected data were randomly partitioned into training (80 %) and testing (20 %) subsets. Four regression algorithms were employed, treating 7 or gamma as the target variable, with injection velocity (um), particle concentration (Cin), and ratio of mean pore size (Dpore) to mean particle size (Dp) as features. The extreme gradient boosting (XGBoost) algorithm showed the best performance. The feature Cin had the highest influence on 7 and gamma, revealing a significant finding previously overlooked. Postmortem analyses revealed qualitative consistencies in 7 results, supporting the existence of critical velocities. Furthermore, 7 results showed a power law relation between 7 and all three features used. An equation was formulated to estimate 7 as a function of these three features. A direct prediction of /3 using these features was established by applying the XGBoost model to predict gamma and then employing an existing power law relationship between /3 and gamma. This study demonstrated that machine learning offers an alternative approach for predicting 7 and /3, which is particularly useful for initial evaluations of clogging potentials and identification of experimental conditions to focus on.