Machine Learning in Sustainable Economic Development: Case Studies and Innovations (KEY IJP************967)

• Kamepalli Sriya Choudary,Dr. Sandhya S

Abstract

The integration of machine learning (ML) into sustainable economic development is becoming increasingly pivotal in addressing global challenges. This paper investigates how ML technologies can be leveraged to promote sustainability while driving economic growth across various sectors. By analysing case studies from industries such as agriculture, renewable energy, transportation, and manufacturing, we highlight the diverse ways in which ML is being applied to enhance resource efficiency, optimize processes, and contribute to broader sustainability objectives. The study also examines the key innovations that have emerged from these applications, focusing on their practical outcomes and potential for scaling. Furthermore, the paper delves into the challenges and barriers faced in adopting ML solutions, including issues related to data availability, regulatory frameworks, and ethical implications. Through these case studies and insights, we aim to provide a comprehensive understanding of the role of machine learning in fostering sustainable economic development and offer recommendations for future research and policy development to maximize its positive impact on society and the environment.