It is a difficult task for practical engineers to calculate the uplift and penetration resistance of two overlapping pipelines that are buried in clay that increases in strength linearly. Hence, in this paper, four regression models, namely long short-term memory (LSTM), bidirectional long short-term memory (BI-LSTM), emotional neural network (ENN), and multivariate adaptive regression spline (MARS) models, are employed to create a data-driven prediction for the uplift and penetration resistance of two overlapping pipelines. For this purpose, a total of 256 samples of uplift conditions and 384 samples of penetration conditions, including three input parameters and one output parameter, are collected from the lower and upper bound finite element limit analysis (FELA) solutions. The predictive strength and robustness of the employed model were evaluated based on various performance metrics, rank analysis, error matrix, Taylor diagram and uncertainty analysis. Sensitivity analysis is also performed to determine the most and least effective parameters. Additionally, the results of the sensitivity analysis indicated that the pipe embedded depth ratio (w/D) was the most significant parameter in both uplift and penetration conditions. The MARS model produces more efficient performance (R2=0.999 and RMSE=0.008 for uplift and R2=0.999 and RMSE=0.009 for penetration condition) for uplift and penetration resistance prediction compared to the BI-LSTM, LSTM, and ENN models. The acquired findings demonstrated that the MARS model predicted the normalized uplift and penetration resistance with reasonable accuracy and yielded superior performance compared to the Bi-LSTM, LSTM, and ENN models. Therefore, it becomes one of the predictive tools practical engineers use in making preliminary decisions about things such as the uplift and penetration resistance of two overlapping pipelines buried in clay, which increases in strength linearly. It also provides a mathematical formulation for easy hand calculations.