This study proposes the development of an electrocontrolled air-oxygen blender pro- totype, designed to provide precise control of the inspired oxygen fraction (FiO2) in patients under mechanical ventilation, especially in intensive care environments. Using dynamic pressure sensors and an adaptive control system, the blender aims to correct real-time variations in the gas mixture, ensuring a fast and accurate response to respiratory demands. Automating this process, combined with the use of Inter- net of Things (IoT) technologies, will enable remote monitoring and optimization of oxygen consumption, providing greater safety and efficiency in clinical practice. The prototype is designed to operate within a FiO2 range of 21% to 100%, with high precision and low response time, positioning itself as a viable and safer alternative compared to conventional devices. Future studies will involve experimental tests and clinical validation to evaluate its performance in real-world scenarios.