Solid-state joining techniques have become increasingly attractive for joining similar and dissimilar materials because it enables further optimization of lightweight components. In contrast to fusion-based joining processes, solid-state joining prevents the occurrence of typical defects such as pores or hot cracking. Machine learning algorithms are powerful tools to identify and quantify relationships between essential features along the process-property chain. In particular, different supervised machine learning algorithms can be used to perform regression analyses and establish correlations between process parameters as well as resulting properties. This can help to circumvent the demand for conducting a vast number of additional experiments to determine optimized process parameters for desired material properties. Additionally, this knowledge can be utilized to obtain a deeper understanding of the underlying mechanisms. In this study, a number of regression algorithms, such as support vector machines, decision trees, random forest and 2^nd-order polynomial regression have been applied to correlate process parameters and materials properties for the solid-state joining process of force-controlled friction riveting. Experimental data generated via a central-composite Design of Experiments, serves as source of two separate data sets: one for training and one for testing the machine learning algorithms. The performances of the different algorithms are evaluated based on the determination coefficient R² and the standard deviation of the predictions on the test data set. The trained algorithms with the best performance measures can be used as predictive models to forecast specific influences of process parameters on mechanical properties. Through the application of these models, optimized process parameters can be determined that lead to desired properties.