Monitoring System that fully integrates multi sensor environmental monitoring and deep-learning based visual inspection, creating a new paradigm for the comprehensive detection of egg spoilage. This system constitutes a departure from traditional approaches to egg management which are limited to either sensory environmental monitoring or leveraging a physical inspection. The Smart Egg Monitoring System takes a multi modal approach in using odor sensing like hydrogen sulphide emission detection, environmental real-time monitoring like temperature and humidity and humidity sensor, and computer vision for defect detection. Environmental data and odor have been sensed by an Arduino Uno which is integrated with multi-sensor and processed and recorded, activating the mist generator to automatically regulate the environment upon storage. At the same time the YOLOv8 deep learning computer vision model is inspecting eggs in real-time, at high speed and analyses the eggs from external defects. The new multi-modal fusion of these two components enables the timely and accurate notification of potential spoilage. The proposed method achieved 97% accuracy, 96% precision, 89% recall, F1-score of 84% and also average inference time of 15 ms, which demonstrates superior performance compared to previous work. Overall, the Smart Egg Monitoring system is intelligent, intuitive and a plug and play system that can be installed in a variety of places, including chicken farms, storage areas and delivery locations all with a view to making sure eggs are safe and maintaining high quality.