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This dataset presents a comprehensive Bengali food segmentation dataset designed to support both semantic segmentation and object detection tasks using deep learning techniques. The dataset consists of high-quality images of traditional Bengali dishes captured in diverse real-life settings, annotated with polygon-based masks and categorized into multiple food classes. Annotation and preprocessing were performed using the Roboflow platform, with exports available in both COCO and mask formats. The dataset was used to train UNet for segmentation and YOLOv12 for detection. Augmentation and class balancing techniques were applied to improve model generalization. This dataset provides a valuable benchmark for food recognition, dietary assessment, and culturally contextualized computer vision research.