Abstract: The balancing problem of type-I assembly lines focuses on finding an assembly line layout with the optimal cost under a given cycle time constraint. Traditional global search-based solution methods are computationally complex and time-consuming. This study addresses a practical scenario where customers are only concerned with the optimal cost rather than the specific layout plan. To this end, the search problem is transformed into a prediction problem, and artificial intelligence algorithms are adopted to predict the optimal cost. The bottleneck of the key factors that constrain the optimal cost cannot be quickly found using feature importance ranking analysis. Firstly, in the case of varying worker cost, a new simulation dataset is constructed by solving an integer linear programming model; secondly, based on this dataset, random forest regression, decision tree, and XGBoost algorithm models are trained to predict the optimal cost using seven parameters including type-I and type-II worker cost; finally, three different methods are applied to rank the importance of features in order to identify the key factors that constrain the optimal cost. The comprehensive performance of three regression algorithms is evaluated using four indicators: R2, RMSLE, EV, and MPE. Results show that the XGBoost algorithm has the highest MPE of 5.12%, while the random forest regression algorithm achieves the lowest MPE of 4.09%, confirming the feasibility of using intelligent algorithms to predict the optimal cost. This study provides a new method for optimal cost prediction using intelligent algorithms. The results of feature importance ranking indicate that the working time of type-I worker has a significantly greater impact on the optimal cost than other factors.