My research focuses on optimizing multilateral well designs with diverse Flow Control Devices (FCDs). These wells pose a challenge due to interconnected variables, reservoir uncertainties, and complex fluid dynamics. Existing methods fall short, often leading to static, simplistic designs. I am dedicated to resolving this by developing a robust optimization framework that combines various algorithms, global sensitivity analysis, and machine learning-based proxy modeling. The core aim is to streamline complex well design optimization while working within limited simulation resources. To achieve this, I'm creating a fast hybrid optimization framework that simplifies the problem by prioritizing control variables based on their expected impact. My goal is to bridge the optimization gap, ultimately improving multilateral well designs and aligning them with dynamic reservoir conditions, leading to enhanced long-term operational and economic efficiency in the industry.