This thesis presents a framework for analyzing spectroscopic optical measurements on sensor structures, specifically plasmonic and porous materials. It addresses the modeling challenges of these materials by applying the Transfer Matrix Method and Effective Medium Approximation to simulate light interactions, enabling precise optical characterization. To optimize parameter extraction and improve computational efficiency and accuracy, various fitting algorithms—including Levenberg-Marquardt, Differential Evolution, and Adaptive Memory Programming for Global Optimization (AMPGO)—are benchmarked. A custom Python-based software tool, incomplete-ease, is developed for streamlined data fitting, visualization, and parallel processing, integrating the models and algorithms for rapid analysis of ellipsometry data. The framework enhances stability and reduces processing time, demonstrating effectiveness across sensor configurations and applications, thus advancing the precision and accessibility of spectroscopic techniques in research and industry.