This paper introduces the Maximum Agreement Linear Predictor (MALP), a regression framework designed to maximize the concordance correlation coefficient (CCC) between predicted and actual values. Unlike the standard least-squares linear predictor (LSLP) which minimizes variance, MALP focuses on optimizing the direct agreement with the predictand. Evaluated through eye and body fat datasets, the model demonstrates that it is a viable alternative for researchers who prioritize predictive alignment and agreement over traditional error-minimization metrics.