The building sector represents the highest share of operational energy consumption across all sectors, with a significant portion attributed to the inefficiency of the existing building stock. In this context, building retrofit plays a crucial role in enhancing energy efficiency and reducing environmental impact. However, conventional models for assessing retrofit scenarios are highly computationally expensive, thereby slowing down the retrofit process. This research addresses this challenge by developing an AI-based surrogate model using Multi-Task Learning (MTL). The proposed MTL model significantly reduces computational costs while simultaneously predicting energy consumption, costs, embodied carbon, and thermal comfort. Additionally, Multi-Objective Optimization (MOO) and Multi-Criteria Decision Making (MCDM) techniques are employed to select optimal retrofit solutions Results demonstrate that the MTL model accelerates the retrofit simulation process from 90 minutes to just 2 seconds, highlighting its potential to streamline and enhance retrofit decision-making processes.