Transverse cracking represents a significant level of distress in Continuously Reinforced Concrete Pavement (CRCP), which is damaging to the pavement’s functionality and durability. This study intends to construct a state-of-the-art hybrid machine learning algorithm that accurately predicts transverse cracking in CRCP by integrating a Gradient Boosting Machine (GBM) with a Genetic Algorithm (GA). The analysis comprised 33 CRCP sections using the Long-Term Pavement Performance (LTPP) database with 20 dependent variables covering traffic, structural, climatic, and performance aspects. The longitudinal and hybrid GA-GBM model was found superiority of baseline models including, standard GBM, Random Forest, Support Vector Regression (SVR), Linear Regression, and Artificial Neural Network (ANN), with RMSE of 0.034 and R² of 0.98926. Annual Average Daily Truck Traffic (AADTT), Kilo Equivalent Single Axle Load (KESAL), and Temperature were found to be the most impactable, sensitive structural variables such as Concrete Layer Thickness (L4 Thickness) Total Thickness. Precipitation and Freeze Index as climatic factors were also identified to have moderate significance. These results demonstrated the effects of traffic, structural, and climatic elements on transverse cracking extremly critecalely. The findings highlight the GA-GBM model's potential to direct data-driven pavement management strategies and show how robust and dependable it is for predictive modeling. To further improve prediction accuracy and applicability, future research should broaden the scope by examining sophisticated machine learning techniques and adding more variables.