Financial transparency and efficient operations are key objectives of the United States public institutions. The current monitoring procedures such as paper-based reporting and manual auditing are not scalable and prompt to monitor intricate large volumes of financial transactions. This paper entails an empirical study on data analytics application on enhancing transparency and efficiency at federal and state agencies. Using peer-reviewed evidence, audit-analytics research, and government reports, it addresses applications like fraud detection, government spending supervision when compromised, auditors monitoring the adherence to published performance commitments; the comparison of full population conclusions in auditors interest bearing dollars. The review indicates that, analytic tools, e.g. supervised and unsupervised machine learning, anomaly detection, network analysis, and hybrid models are used to support proactive risk-based oversight by detecting outlier(s), ranking investigations, and reducing improper payments. Poor data quality, disjointed systems, the absence of metadata, inconsistent analytical information, and institutional governance weakness are some of the limitations mentioned in this paper. Results show that successful implementation will necessitate coordinated investments in data standardization, workforce expertise, computational infrastructure, and model-governance frameworks that incorporate human-in-the-loop review for transparency and accountability. With operationalization of the methodological improvements, data analytics may transform financial management in the public sector by replacing the retrospective audit with continuous, evidence-based regulation. The paper identifies the transformative quality of analytics as a technology and governance instrument, and the need to reformulate policy and institutional framework to be able to realize its full potential.