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Personalized cooking recipe recommendation systems offer the potential to improve dietary choices for unhoused individuals and those transitioning out of homelessness. However, existing systems often neglect the needs of users with minimal cooking experience, providing little guidance during meal preparation. This study proposes the development of an intelligent cooking assistant system designed to offer realtime, step-by-step support throughout the cooking process. The system integrates a Raspberry Pi 5 mini-computer with a Raspberry Pi AI HAT+ (AI HAT+) and Raspberry Pi AI Camera (AI Camera), strategically mounted above the cooking area to continuously monitor culinary activity. At its core, the assistant utilizes a deep learning image classification model built on Ultralytics’ You Only Look Once version 11 (YOLO11) framework, trained on a curated dataset of 1,339 images collected during the preparation of chicken teriyaki and pasta dishes. The model achieved 100% precision and 99% recall of identifying all six cooking states utilized in this work, resulting in an average confidence accuracy of 91% during real-time tests. The system is intended to enable greater culinary independence among individuals with little cooking experience, such as those affected by long-term homelessness.