The main objective of this project involves designing an intelligent egg incubator which achieves efficient incubation through precise environmental control of multiple egg types. The incubator maintains both temperature and humidity levels automatically which allows perfect fetal growth without needing continuous maternal oversight. Our proposed Smart Egg Incubator system combines Internet of Things (IoT) functionality with real-time visual monitoring and machine learning defect detection to achieve better hatchability rates alongside enhanced user experience and automated operations. The system uses a NodeMCU microcontroller to keep temperature at 36  \(^\circ \) C and humidity at 32% through sensor-based closed-loop control. Users can use mobile and web interfaces through Blynk cloud server to monitor and control the system remotely for immediate parameter changes and alert notifications. Early detection of cracked and infertile eggs and contaminated eggs is possible through real-time visual monitoring with embedded convolutional neural networks which stops wasted incubation cycles. Testing results show the system delivers better environmental stability and faster anomaly detection and higher hatch rates than both manual and semi-automatic systems. The modular design enables simple adjustments to accommodate different egg sizes and species with future plans to add renewable energy capabilities for off-grid use and lightweight AI models for edge-based real-time decision making. This approach not only enhances productivity and scalability in poultry farming but also contributes to sustainable, energy-efficient incubation technology.