This study investigates how internal audit functions have transformed in Philippine local governments following the COVID-19 pandemic and explores their implications for local governance in the municipalities of Odiongan and San Agustin in Romblon, Philippines. Using a descriptive-correlational research design, the study examined the relationships among internal audit standards, roles and functions, methods, and tools. Data were collected through a validated 4-point Likert-scale questionnaire administered to 50 purposively selected stakeholders directly involved in or affected by internal audit processes. The survey instrument, based on established theoretical models, underwent content validation and reliability testing. Quantitative data were analyzed using SPSS for descriptive statistics, normality testing, and non-parametric correlation and difference analysis. The findings revealed significant disparities in internal audit transformation between the two municipalities, with Odiongan demonstrating a higher level of transformation in terms of standards, methodologies, tools, and functions. Statistical analysis confirmed that these differences were significant, suggesting stronger institutional support and internal audit maturity in Odiongan. Strong correlations across all internal audit components underscored their interdependence and validated theoretical models related to internal audit transformation. The results support global literature indicating that successful internal audit transformation depends on leadership, resource capacity, and organizational commitment. These findings provide actionable insights for policymakers and local governments seeking to institutionalize transformative internal audit systems to strengthen local governance through comprehensive capacity building and standardized frameworks. Although the study's focus on two municipalities limits generalizability, it offers valuable insights and suggests that future research should include additional local government units and longitudinal data for broader applicability.