Accurately predicting the deck condition rating of a bridge is crucial for effective maintenance and repair planning. Despite significant research efforts to develop deterioration models, a nationwide model has not been developed. This study aims to identify an appropriate machine learning (ML) algorithm that can accurately predict the deck condition ratings of the nation's bridges. To achieve this, the study collected big bridge data (BBD), which includes NBI, traffic, climate, and hazard data gathered using geospatial information science (GIS) and remote sensing techniques. Two sets of data were collected: a BBD for a single year of 2020 and a historical BBD covering a five-year period from 2016 to 2020. Three ML algorithms, including random forest, eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), were trained using 319,404 and 1,246,261 bridge decks in the BBD and the historical BBD, respectively. Results showed that the use of historical BBD significantly improved the performance of the models compared to BBD. Additionally, random forest and XGBoost, trained using the historical BBD, demonstrated higher overall accuracies and average F1 scores than the ANN model. Specifically, the random forest and XGBoost models achieved overall accuracies of 83.4% and 79.4%, respectively, and average F1 scores of 79.7% and 77.5%, respectively, while the ANN model achieved an overall accuracy of 58.8% and an average F1 score of 46.1%. The permutation-based variable importance revealed that the hazard data related to earthquakes did not significantly contribute to model development. In conclusion, tree-based ensemble learning algorithms, such as random forest and XGBoost, trained using updated historical bridge data, including NBI, traffic, and climate data, provide a useful tool for accurately predicting the deck condition ratings of bridges in the United States, allowing infrastructure managers to efficiently schedule inspections and allocate maintenance resources.