Record linkage and clustering are essential for integrating data, eliminating redundancy, and resolving entities across industries such as healthcare and finance. This study evaluates advanced fuzzy matching techniques using the fuzzymatcher library in combination with TF-IDF and KNN algorithms to classify linked records effectively. It also looks at how well clustering techniques like Gaussian Mixture Models (GMM), Fuzzy C-Means (FCM), and K-Means perform in different scenarios. Principal Component Analysis (PCA), silhouette scores, accuracy, and precision are important evaluation metrics that offer a thorough analysis of these techniques. The results show that each strategy has unique advantages. Because of its great efficiency and quick execution durations at lower cluster counts, FCM is appropriate for short studies. When clusters are well-separated, K-Means provides solid performance for structured datasets, achieving improved silhouette scores and accuracy. GMM, on the other hand, excels at modeling complex or overlapping clusters due to its flexibility but incurs higher computational costs. These results emphasize the importance of selecting algorithms that align with specific dataset characteristics and operational requirements. This research contributes to large-scale data management methodologies by offering practical insights into improving decision-making and ensuring data reliability. To improve scalability and resilience, future research should concentrate on creating hybrid models that incorporate the advantages of these approaches, improving initialization tactics to increase stability, and evaluating their applicability across various datasets. The paper establishes the foundation for optimizing record linking and clustering solutions in complicated data settings by tackling these issues.