Accurate interpretation of urine test strips is vital for point-of-care (POC) diagnostics; however, manual readings are constrained by subjectivity, inter-observer variability, and inconsistent environmental conditions. This study presents a systematic comparison of three segmentation paradigms, K-means clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and the Segment Anything Model (SAM), for automated identification and delineation of reagent pads on ACON MISSION 10-parameter urine test strips. A dedicated portable imaging system was developed using a Raspberry Pi 4 with controlled LED-based illumination inside a fully enclosed 3D-printed chamber. Analytically prepared synthetic urine solutions (S1–S4) spanning clinically relevant concentration ranges for seven analytes, including glucose (500–2000 mg/dL), protein (50–200 mg/dL), and occult blood (0.75–3.0 mg/dL), were used to generate a dataset of 120 images across five test conditions. All experiments were performed with five replicates per condition and five image captures per strip to assess reproducibility. From aggregate confusion-matrix analysis, SAM achieved 99.34% pixel-wise accuracy (± 0.15%), 94.34% precision, 87.71% recall, and an F1-score of 0.909, compared to K-means (96.57% accuracy, 52.69% precision, F1 = 0.622) and DBSCAN (94.56% accuracy, 38.46% precision, F1 = 0.513). The mean Intersection over Union (IoU) of 83.32% (± 0.54%) further confirms SAM's superior ability to delineate reagent pad boundaries with high fidelity. Statistical analysis revealed that SAM maintained a coefficient of variation below 0.5% across all concentration levels, demonstrating robust invariance to chromatic shifts. While this proof-of-concept utilizes synthetic samples under controlled conditions, the results demonstrate the feasibility of deploying transformer-based segmentation on low-cost embedded hardware for objective, automated urinalysis.