This study assesses the multi-target regression (MTR) using proposed Tree-based Pipeline Optimization Tool – MTR (TPOT-MTR). Six (6) publicly available datasets were used to examine the performance of the aRRMSE. Further, Pearson correlation was employed in order to determine the significance of some of the demographic characteristics on relationship towards targets. The analysis reveals that TPOT-MTR generally lags behind other tools like AutoGluon AutoML in minimizing aRRMSE, although it performs better in term of the correlation context across all datasets. The result in JURA dataset, TPOT-MTR achieves the lowest ARRMSE, indicating a prediction model that is more performed, also there is a summary of the findings, emphasizing the potential of the proposed methods to advance the field of multiple target regression and suggesting avenues for future research. Overall, TPOT-MTR demonstrates robust performance and adaptability, making it a valuable tool for predictive modelling despite some limitations in specific datasets.