Abstract: Static cone penetration test is often used in engineering practice to obtain relevant geological information. Yet the relatively sparse arrangement of test holes results in a large number of unknown areas in the site, which could affect the accurate judgment of the actual geological information in engineering design and construction. Therefore, this study proposes MCP-DD (minimax concave penalty-distance determined) prediction method for single bridge static cone penetration curve of rock and soil. It contains three main parts: 1) screen neighboring training data through the neighborhood radius search algorithm; 2) compute trend estimation functions of selected data through the modified MCP algorithm based on the B-spline basis function; 3) weight the trend estimation function by the spatial correlation function to obtain an integrated prediction model. This method was used to predict the single bridge static cone penetration curve of a certain engineering site. Results show that compared with the traditional linear interpolation method, the proposed method exhibits a higher coefficient of determination R². The mean absolute error MAE reduction reduced by 26.8% ~55.8% and the root mean squared error RMSE reduces by 25.2% ~54.9%. The optimal range for predicting the radius R₀ is 25.0~37.8 m. Increasing average number of relevant data points leads to smaller average relative distance between the predicted points and the relevant data points, thus bettering the prediction performance of the model.