Infertility continues to pose significant medical and social challenges worldwide, prompting considerable advancements in assisted reproductive technologies (ART), particularly In-Vitro Fertilization (IVF). The success of IVF critically depends on precise environmental conditions within incubators, demanding real time, accurate monitoring to optimize embryo viability. This chapter discusses the integration of Internet of Things (IoT) and machine learning (ML) techniques into IVF laboratory incubators to achieve real-time monitoring and predictive control of critical parameters such as CO₂ concentration, temperature, humidity, and volatile organic compounds (VOC). An advanced prototype consisting of a universal sensor bank and a sophisticated controller unit, incorporating Arduino and Raspberry Pi platforms, was designed and implemented. Real-time sensor data was transmitted to cloud storage and made accessible remotely via a user-friendly graphical user interface (GUI). The chapter emphasizes the development and validation of artificial neural network (ANN)-based predictive models, which reliably forecast the alarming and recovery times following incubator door openings. The ANN model, trained using extensive datasets collected from prolonged incubator operation, demonstrated high prediction accuracy (R2 values exceeding 0.9), enabling proactive environmental adjustments that significantly reduced recovery times and enhanced embryo viability by approximately 12%. The successful integration of IoT and ML offers substantial clinical and operational benefits, aligning with India’s “Make in India” campaign, ultimately advancing IVF technology’s effectiveness and accessibility.