The stability of financial institutions is seriously threatened by the exponential rise in financial fraud, which makes the development of reliable and effective fraud detection methods imperative. This study compares how well different machine learning algorithms perform in identifying financial fraud, with an emphasis on supervised and unsupervised methods. Finding the best algorithms for real-time detection in terms of accuracy, precision, recall, and processing efficiency is the goal. Utilized was a data set of past financial transactions with a known percentage of fraudulent cases. Several machine learning techniques, such as Decision Trees, Random Forests, AdaBoosting, and Logistic Regression, were used in the methodology to train and test the models. Furthermore, the performance measures Recall, F1 score, precision, and confusion matrix were employed to assess each model’s efficiency. The findings show that random forests attained the maximum accuracy in supervised learning. Random Forest provided a better trade-off between performance and transparency, however there were differences in the model’s interpretability and efficiency. After implementing various algorithm on the model, Random Forest has the maximum accuracy which is 99.97%.