Real-time prediction of product quality or other key performance indicators is critical to ensuring high-quality products and increasing economic profit. In this study, a new locally weighted partial least-squares (LW-PLS) method using optimal weighting distance-based similarity, denoted as OLW-PLS, is proposed. OLW-PLS is a nonlinear just-in-time modeling method, which can handle process collinearity, nonlinearity, and time-varying characteristics. In OLW-PLS, a weighted PLS regression model is constructed based on the optimal weighting distance-based similarity, which considers variable interactions and nonlinear dependencies between the input variables and the output in an optimal manner. The feasibility and effectiveness of the proposed OLW-PLS method were validated through its applications to a numerical example, an industrial ethylene fractionation process, and a pharmaceutical process. The application results have demonstrated that OLW-PLS has superior prediction performance than the conventional PLS, LW-PLS, and CbLW-PLS.