Health insurance claim fraud remains a significant challenge for insurance providers, leading to substantial financial losses and increased premiums for policyholders. Traditional fraud detection methods, such as linear regression, logistic regression, k-nearest neighbors (KNN), support vector machines (SVM), and random forests, have proven effective to some extent but are limited in their ability to adapt to the evolving nature of fraudulent activities. This research proposes a novel approach by harnessing the power of Artificial Neural Networks (ANNs) to enhance health insurance claim fraud detection. The study leverages a comprehensive dataset comprising medical and financial information, claim histories, and behavioral patterns. ANNs, specifically deep neural networks, are employed to capture intricate, non- linear relationships within the data, allowing for more precise and adaptable fraud detection. The research showcases the capability of ANNs in handling the complexity of healthcare data, which often exhibits high dimensionality and intricate interdependencies. The paper presents a comparative analysis of ANN-based models against conventional algorithms, emphasizing the superior performance of ANNs in terms of accuracy, sensitivity, and specificity. ANNs exhibit remarkable resilience to overfitting and the ability to generalize from limited training data, which is crucial for the dynamic landscape of health insurance fraud.