The multi-criteria data envelopment analysis (MCDEA) model emerges within the framework of data envelopment analysis (DEA) from a multi-objective perspective. However, it is pertinent to acknowledge that the criteria embedded within the MCDEA model often exhibit inherent conflicts, rendering the concurrent fulfilment of all objectives an elusive feat. One of the important topics in DEA is the improvement of inefficient units, and multi-criteria models are usually used in this area. This study introduces an enhanced model tailored to address underperforming entities through the strategic incorporation of the goal programming (GP) approach. Within this novel model, underperforming decision-making units are conceptualized as the pivotal goals. The principal objective revolves around the concurrent estimation of the maximum achievable output while accommodating variations in input parameters and enhancing operational efficiency. Furthermore, in response to the intricacies inherent to multi-criteria improvement models, the emphasis is placed squarely on GP formulation as a means to overcome these challenges. In doing so, the model is transformed into a single-objective function, thereby simplifying the decision-making process. This research expounds upon the proposed approach, offering an intricate delineation of its intricacies. Furthermore, the efficacy of the proposed model is demonstrated through a detailed numerical example, elucidating its practical applicability in addressing the intricate issues associated with multi-criteria performance enhancement within the context of data envelopment analysis.