aspg-3-2698-1791417793 20261008000313 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2024 15 2 Yuliadi Erdani Lecturer/Prof. Associate, Politeknik Manufaktur Bandung, Indonesia Ankit Kumar Dubey Assistant Professor, Computer Science and Engineering, Baderia Global Institute of Engineering and Management, Jabalpur MP, India Aws Zuhair Sameen College of Medical Techniques, Al-Farahidi University, Baghdad, Iraq Saksham Sood Centre for Interdisciplinary Research in Business and Technology, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India. Ramanchi Radhika Professor GITAM School of Business GITAM University, Hyderabad Telangana, India Mohammad Ahmar Khan Department of Management Information System Dhofar University Salalah, Oman The research article "Harnessing the Power of Machine Learning to Refine Data Fusion Processes for Better Accuracy and Speed" proposes integrating different machine learning methods to improve data fusion. The suggested method uses an ensemble learning strategy, a deep learning-based fusion model, SVMs for data combining, CNNs for image and time-series data combining, and RNNs for time-series data combining. For best efficiency, each algorithm is carefully constructed utilizing mathematical concepts. Deep learning shines on complicated datasets, whereas the ensemble approach, which uses several models, is more accurate. CNN handles visual data better than RNN does sequence data. However, SVM shines in multidimensional domains. These reliable and adaptive solutions can tackle various data fusion difficulties. This approach outperforms others in processing speed, accuracy, precision, memory, and F1-score. Finding a balance between computer complexity and human satisfaction enhances dependability, data duplication, and quality. This novel technique transforms machine learning-powered data fusion. Another benefit is better data integration in complicated systems. 2024 208 220 2698 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.150219 https://www.americaspg.com/journal/3/article/2698 https://www.americaspg.com/storage/11713714293.pdf