The proliferation of social media platforms has led to an increase in spammer fake profiles, posing significant security, privacy, and trustworthiness concerns. Traditional manual monitoring and content filtering techniques are insufficient to combat this growing issue, necessitating the development of more efficient and accurate detection methods. Machine learning techniques have been increasingly employed for this purpose, demonstrating promising results in identifying spammers and fake profiles. This paper presents a novel approach for spammer fake profile detection using Artificial Neural Networks (ANNs) to enhance the accuracy of the detection process. Our proposed ANN-based method addresses the challenges associated with spammer fake profile detection, such as the dynamic nature of spammers, data heterogeneity, scalability, and imbalanced datasets. We evaluate the performance of our method on real-world datasets and compare it with existing machine learning techniques, demonstrating its effectiveness and superiority in detecting spammers and fake profiles with higher accuracy. This research contributes to ongoing efforts to secure social media platforms, ensuring the trustworthiness of online content and providing a safer user experience.