<![CDATA[This study investigates the potential of adaptive machine learning algorithms for processing high-dimensional data across various fields, directly supporting the advancement of the United Nations Sustainable Development Goals (SDGs) such as healthcare, economic growth, and sustainable cities. The core objectives are to critically review existing methods, tackle the challenges posed by large datasets, and project future developments in adaptive machine learning technologies. Through a comprehensive analysis of diverse algorithms including autoencoders, deep learning, reinforcement learning, and ensemble methods this research evaluates their efficacy in managing the complexities of large-scale data. Results demonstrate that while deep learning models provide the highest accuracy, they also demand considerable computational resources. Conversely, ensemble methods and autoencoders show competitive performance with greater efficiency, although reinforcement learning exhibits adaptability at the cost of reduced scalability. The findings advocate for enhanced focus on improving the efficiency, generalization capabilities, and interpretability of these algorithms to better accommodate the increasing complexity of data-driven environments. Promising applications identified include enhancing diagnostic accuracy in healthcare, optimizing financial analytics, and advancing autonomous system technologies. The study concludes that significant progress in adaptive machine learning will be crucial for achieving SDGs by enabling more effective and efficient data analysis solutions, thereby fostering sustainable development across multiple domains.]]>
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