This research conducts a comprehensive comparative analysis of machine learning algorithms for online fraud detection with a focus on sustainability. As the digital landscape evolves, the need for robust fraud detection mechanisms becomes imperative. The study delves into the efficacy of various machine learning models in identifying fraudulent activities, considering their environmental impact and resource efficiency. Leveraging a diverse dataset from online transactions, the research evaluates the performance of sustainable machine learning algorithms, including their accuracy, precision, recall, and computational efficiency. The findings contribute valuable insights into developing environmentally conscious and effective fraud detection systems, aligning with the principles of sustainable development in the realm of cybersecurity and financial technology.