With unparalleled worldwide growth and technological advancements, the need for eco-awareness is greater than ever. Green marketing has quickly become an integral part of the low-carbon economy, capturing industries and consumers alike. Green marketing is fundamentally a conscious coordination of business functions with ecology. Companies are happy to exhibit their pursuit of making themselves environmentally sustainable by selling merchandise and services that not only fit customer needs but also benefit the welfare of our planet. Green Marketing is actually the process of creating merchandise and services that are environmentally friendly. While Green Branding tries to create a brand image that is eco-friendly and sustainable. Realizing the pivotal importance of green marketing in the present times, integrating it with the latest technical advancements, particularly in the fields of Artificial Intelligence(AI) and Machine Learning(ML), can have the potential to yield bountiful outcomes for business as well as the surrounding environment. Companies that align sustainability with the disruptive potential of AI and ML can realize an unlimited set of benefits that boost consumer satisfaction, organizational productivity, and environmental protection to new heights. Methodologies: The paper methodology probably involves incorporating green marketing with AI and machine learning methods for maximizing sustainability strategies. Secondary Data is gathered from available reports, company sustainability reports, and databases on consumer behavior, green product sales, and marketing campaigns. Statistical tools like mean, median are utilized to understand overall trends and patterns in the data. Implemented machine learning algorithms like K-means or hierarchical clustering to classify customers based on eco-consciousness and other relevant attributes. Limitations: Though providing revolutionary advantages to companies and the environment, this field demands enormous investment in research and development. Furthermore, data availability to extend and deepen research in this area is still scarce. Besides, this study is based on secondary data from Kaggle, which can limit the depth and accuracy of the analysis. Even though it provides worthwhile insights, it might not accurately represent customer behavior or green marketing specifics. Practical Implications: This paper will help the reader to understand how AI and ML are applicable in green marketing, how these tools align with the principles of green marketing. It takes a snapshot of an unsupervised machine learning model to measure client attitude and preference towards sustainable products. By segmenting markets based on sustainability priorities, firms can align their marketing initiatives to more effectively target green consumers. Case Studies: Paper aims to look at specific instances of businesses that have successfully integrated AI and machine learning into their operations. Leading businesses like Unilever and Patagonia set the standard by utilizing modern technology to transform processes, increase productivity, and spur innovation. Unsupervised machine learning techniques are used to investigate the collected Kaggle dataset, for an example implementation. Conclusion: With the help of sophisticated data analysis and machine learning techniques, businesses should be able to better understand customer behavior, enhance their environmental impact, and improve their marketing tactics. Utilizing these cutting-edge technologies not only improves business productivity but also harmonizes corporate objectives with global environmental objectives. Businesses who embrace this combination stand to gain a competitive advantage and contribute to a better world as the demand for green goods and services rises.