Data mining has become a cornerstone in extracting meaningful patterns from large datasets. With the exponential growth of data, traditional data mining approaches face challenges in terms of efficiency and accuracy. This study aims to enhance pattern recognition in data mining by introducing a novel hybrid algorithm that combines the strengths of both supervised and unsupervised learning methods. Our research utilized a combination of decision tree algorithms and k-means clustering to process a diverse set of real-world datasets obtained from various industries. The findings indicate that the hybrid approach significantly improves the accuracy of pattern recognition while reducing computational time by 20% compared to traditional methods. By applying this enhanced method, we achieve more reliable predictions and insights that can support decision-making processes in business and research domains. The conclusion emphasizes the potential of hybrid algorithms to transform data mining, making it more robust and adaptable to complex datasets. Future work will explore the integration of deep learning techniques to further refine and expand the applicability of these methods.