Complaint management refers to the process that makes dissatisfaction on the part of the customer an opportunity for growth and development. Traditionally, it was a slow process and error-prone because it involved manual operations. The process becomes challenging, especially when inputs are multimedia, such as video or images. However, in situations such as fighting engagement and health-related problems, when it is essential to resolve matters urgently, these manual processes take too much time, and the situation worsens. To solve this problem, we propose an approach using Image Captioning and GenAI APIs to categorize the issues in their particular category, which is the best possible approach. The present study focuses on combining the efficientB0 model with transformer-based models for image captioning that are applicable as part of an AI-powered solution to image captioning. We applied this model on large image datasets like Flickr30k and Flickr8k to generate descriptive captions about multimedia complaints. In comparison, our proposed model was very accurate on Flickr30k and Flickr8k datasets with a 92.67% result, outperforming Wei Liu’s Transformer model on the MS COCO dataset, which achieved 91.0%. Subsequently, using a sophisticated GPT model, we shall analyze those captions with their images to classify complaints appropriately under themes such as cleanliness, safety, or even other relevant themes. This research illuminates the revolutionizing capabilities of AI in complaint categorization, which makes possible the enhancement of the speed and reliability with which complaints can be categorized toward improving service quality and satisfaction of customers, ultimately driving meaningful change in complaint-handling activities.