This study delves into the evaluation of food laboratories through the implementation of a Maturity Model (MM) across two assessment cycles spanning a 6-year period. Accreditation, a critical aspect indicating a laboratory's adherence to established standards and competence in testing, is accompanied by the concept of maturity. Maturity, in this context, represents the level of development and effectiveness of a laboratory's quality management system, encapsulating operational efficiency, result accuracy, and customer satisfaction. Higher maturity levels not only optimize internal processes but also instill confidence in stakeholders, including customers and regulatory bodies. The MM, developed from Analytes Accurate Certain Score (AACS) and System Maturity Scores (SMS), assesses the development and implementation of quality management processes. The AACS focuses on the 4M framework: Manpower, Method, Machine, and Material, covering the ten most frequently tested analytes, while the SMS considers internal audit and external assessment findings, along with overall proficiency testing performance. Comparing commercial and government laboratories, both types demonstrated similar performance, falling within the "Emerging" level. However, the commercial sector exhibited a slightly higher Average MM score (67%) compared to government laboratories (64%), implying better accuracy. Lab S, a recently accredited commercial food lab, stood out with an exceptional MM score of 82%, achieving a "Leading" level with high SMS and AACS scores. Principal Component Analysis (PCA) served as an indicator for evaluating Proficiency Testing (PT) performance over time, while Monte Carlo Simulation (MCS) determined Measurement Uncertainty (MU) based on PT data. In the 2021 PT Scheme FODAS 1 for benzoic acid as the analyte, with 17 out of 19 participants achieved satisfactory results with MCS compared with only 13 who achieved satisfactory results previously. The study also explored MU assessment for various analytes, including caffeine, sorbic acid, calcium, chloramphenicol, and arsenic, using bottom-up, top-down, and MCS approaches. Consistent MU values were observed for benzoic acid and caffeine using both bottom up and top-down methods. In Sorbic Acid and Calcium analysis, the top-down approach resulted in higher MU, influenced by precision and trueness studies, while the MCS approach set maximum limits for participant comparison. In conclusion, this study emphasizes the importance of tailored approaches for accurate MU assessment, offering insights for laboratories to enhance measurement performance and quality assurance practices. The findings contribute valuable insights into general trends observed between government and commercial laboratories, Highlighting the need for continuous monitoring and improvement to maintain high-quality services and reliable results in both types of laboratories.