This paper presents a novel approach for real-time aquaculture net pen monitoring by integrating vision-based AI with large language models. Traditional monitoring methods, which rely heavily on manual inspection and semi-autonomous systems, are often labor-intensive and inefficient. This work uses advanced AI techniques, combining YOLO-based deep learning model for detecting net defects such as biofouling, vegetation, and net holes, with large language model ChatGPT-4 to interpret and summarize inspection results in real-time. The proposed approach provides a real-time aqua-net inspection that enhances the accuracy and speed of net inspections while minimizing human intervention. Experimental results demonstrate significant improvements in detection precision resulting in mAP score of 0.9701 on our custom aqua-net dataset, operational efficiency, and automated report generation, highlighting the potential of this integrated approach to transform aquaculture management and promote sustainability.