AI, Machine Learning, and Big Data-Driven Innovation in Science and Engineering

Abstract

The rapid growth of Artificial Intelligence (AI), Machine Learning (ML), and Big Data has transformed scientific and engineering research by enabling data-driven analysis, automation, and innovation. However, many existing studies address these technologies in isolation, limiting their overall impact. This study aims to develop and evaluate an integrated AI, ML, and Big Data–driven research framework that supports end-to-end experimentation and innovation. The proposed method combines large-scale data acquisition and preprocessing, scalable Big Data analytics, advanced AI and ML modelling, and iterative experimental validation. Experimental results demonstrate consistent improvements in key performance indicators, including higher predictive accuracy, reduced error rates, shorter processing times, and nonlinear performance gains with increasing data size. Furthermore, the results show that iterative integration of AI and Big Data significantly enhances an innovation impact index, indicating cumulative and sustained innovation outcomes. The discussion highlights the synergistic effects of combining AI, ML, and Big Data, where Big Data enables scalability, ML ensures stable learning, and AI delivers superior accuracy and efficiency. In conclusion, this study confirms that a holistic and iterative integration of AI, Machine Learning, and Big Data not only improves technical performance but also systematically drives innovation in science and engineering. The proposed framework provides a transferable foundation for future data-driven research and high-impact applications.