An intelligent framework combining Autoencoder Neural Networks and the Dragonfly optimization algorithm, which would be put forth to this research to combat effective credit card fraud by detecting fraudulent transactions quickly through extracting important features and patterns in transactional data with the help of Autoencoder Neural Networks. The Dragonfly optimization algorithm enhances the recital of the archetypal in question by refining the hyperparameters of the autoencoder archetypal. In doing so, the algorithm improves adaptability to emerging fraud patterns and always gives strong generalizations. Critical experiments are conducted that show that the framework has enormous precision, accuracy, F1 score, specificity, as well as recall while trying to detect credit card fraud as accurately as possible, reaching a maximum accuracy of 98%. This framework, therefore, will prove to be a good defence against credit card fraud by finally protecting financial interests, based on drawing the benefits of Autoencoder Neural Networks and Dragonfly optimization.