Receptor tyrosine kinases (RTKs) are key regulators of cellular functions, such as differentiation, migration and proliferation. Dysregulated RTK activity contributes to various diseases, including neurological disorders and cancer, for which small molecule inhibitors are often used as therapeutic drugs to manage conditions involving constitutively active RTKs. Preclinical development of these inhibitors, unfortunately, often faces uncertainty, high costs, and time constraints, primarily due to the extensive in vitro and in vivo testing of numerous chemical compounds. To tackle the challenge of shortlisting potential drug candidates, we developed kinCSM-RTK, which incorporates two machine learning models to predict small molecule pKi and pIC50 values, estimating the inhibitory potency of small molecules against RTKs. Our proposed machine learning models have shown a robust and generalizable predictive performance. Specifically, the models achieved Pearson's correlation coefficients of 0.773 and 0.762 for pKi prediction, under 10-fold cross-validation (CV) and an independent blind test, respectively. Similarly, for pIC50 prediction, the models yielded coefficients of 0.773 in 10-fold CV and 0.768 in the independent blind test. In addition, we aimed to understand these results through post hoc explanation analyses. In our explanation analyses, we observed that the proximity and quantity of aromatic interactions have correlated with stronger RTK inhibition, providing important insights for drug design targeting RTKs. Accordingly, we made our models publicly accessible on a user-friendly web server at https://biosig.lab.uq.edu.au/kincsm_rtk/.