Road crashes are one of the most critical issues that pose a serious threat to our daily life; Crash occurrences prediction is a key role in designing efficient intelligent transportation systems. In this study, we aim to analyze road crash events for experienced and novice drivers under several weather conditions during multiple driving simulations that have been conducted using a desktop driving simulator. This work outlined the effect of snow and rain conditions on driver behavior by endorsing real-time driver data namely: wheel angle position, throttle pedal position and brake pedal position. Moreover, optimized modeling strategies using the deep learning algorithm Multilayer Perceptron (MLP) and Support Vector Machine (SVM) along with Bayesian Networks (BN) models have been developed to analyze crash events. To the authors’ knowledge, there has been a limited interest at assessing the impact of both snow and rainy weather conditions on the occurrence of crash events while providing a critical analysis for experienced and novice drivers based on driver entries; this approach fill the research gap of the combined effect of driving experience and weather conditions on road crash occurrence. The findings depict superior performances have been obtained when adopting the proposed strategy. As a whole, new insights into weather-induced crash events’ investigation for experienced and novice drivers have been acquired and can be endorsed for designing effective crash avoidance/warning systems.