This article presents a comparative study on the performance of three widely used speckle demodulation algorithms, average intensity algorithm (AIA), normalized inner product coefficient (NIPC), and sum of squared differences (SSDs), for multimode optical fiber sensors under identical experimental conditions. We examine how these algorithms perform when analyzing speckle patterns from a polymer optical fiber exposed to variations in both refractive index and pressure. Unlike prior works that typically focus on a single algorithm applied to a specific sensing task, our study evaluates all three methods side-by-side across the same operational range. Through this approach, we provide insights into each algorithm’s suitability in terms of sensitivity, dynamic range, and computational load. Additionally, we explore how image segmentation into regions of interest (ROIs) can fine-tune sensor response and even reveal directional speckle shifts tied to different physical phenomena. Our findings offer practical guidance for selecting appropriate demodulation techniques in multimodal sensing applications and highlight the potential of speckle-based systems for robust, low-cost, multiparameter fiber-optic sensing.