Data mining has emerged as a pivotal field in computer science, providing tools and methodologies for extracting meaningful patterns from large datasets. This study addresses the increasing complexity of data and the demand for more efficient pattern recognition techniques. Our objective was to develop a hybrid approach that integrates both clustering and classification methods to enhance data analysis outcomes. We implemented a hybrid algorithm that combines k-means clustering with support vector machines (SVM) for classification tasks. The methodology involved preprocessing a dataset to ensure data quality, applying k-means to identify natural groupings, and subsequently using SVM to classify data points with high accuracy. Our findings demonstrate that this hybrid approach significantly outperforms traditional methods in terms of accuracy and processing time. The effectiveness of the algorithm was tested across three diverse datasets, showing consistent improvements in pattern recognition capabilities. In conclusion, the proposed hybrid method offers a robust solution for data mining tasks, paving the way for more efficient data analysis in various fields such as finance, healthcare, and social network analysis. Future research may focus on further optimization of the algorithm and testing in real-world applications.