Our study on Legal Judgment Prediction (LJP) focuses on indictments, designing innovative tasks for prosecutors to predict reasons, imprisonment, fines, and penalty types. We investigated multi-task learning (MTL), Low-Rank Adaptation (LoRA), and its quantized version (QLoRA). Results show that the MTL framework, combined with LoRA and QLoRA, excels in prediction while optimizing resource use. Error analysis in the Reason field of TWPJD dataset was conducted to guide future predictions. Future work will focus on balancing sub-task performance and collecting more investigative data to enhance legal AI applications.