Corporate social responsibility (CSR) implementation has been widely acknowledged as playing a key part in enhancing firm value as well as achieving sustainable development. However, up to now the extant works in the literature have yielded non-conclusive results regarding the relationships between CSR and firm value. One of the possible reasons is that the studies ignore the multi-dimensional characteristics of CSR-that is, they merely utilize a singular synthesized indicator as a proxy to represent the corporate's CSR performance as being unreliable and problematic. Thus, this study breaks down CSR into numerous dimensions and further examines each dimension's impact on firm value. By doing so, managers can allocate their firm's valuable resources to suitable areas so as to increase its reputation and value. In addition, this research sets up an artificial intelligence (AI)-based fusion model, grounded by fusion learning theory that aims at complementing the error made by a singular model, to examine the relationship between CSR's multidimensional characteristics and firm value. Through different combinations of adopted strategies, users can realize the most representative features from an over-abundant database. value of total liabilities, and sales to total assets) so as to discriminate between healthy corporates and non-healthy corporates. Although this model performs a satisfactory job in forecasting quality, it also comes with some statistical challenges, such as linear separability, independent predictors, and multivariate normality that usually do not hold in real applications. To overcome these obstacles, the literature has proposed the linear probability model (LPM) and logit or probit regression models. Meyer and Pifer [4] employed LPM to handle the task of the corporate financial bankruptcy forecasting task. Martin [5] assessed banks' financial troubles by relying on a logit model. Dimitras et al. [6] provided a detailed review of statistical-based approaches in financial crisis forecasting, indicating that the logit model achieves optimal forecasting performance.In contrast with those studies that have broadly examined financial crisis prediction and credit risk forecasting, very few have looked into firm value forecasting. Poor firm management is widely recognized as being the main trigger for a financial crisis, and thus firm value can appropriately reflect the quality of corporate management. If managers can run their business with efficiency and target maximizing shareholders' wealth, then investors will likely pay more than average to own their stock.