This paper presents a comparative study of the performance of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Artificial Neural Networks (ANN) in the detection of brain tumors from medical imaging data. The increasing prevalence of brain tumors necessitates the development of accurate and efficient diagnostic tools. Leveraging the capabilities of deep learning, particularly CNNs, RNNs, and ANNs, offers promising avenues for enhancing diagnostic accuracy and facilitating early intervention. Through rigorous experimentation and evaluation on a benchmark dataset, we analyze and contrast the effectiveness of these neural network architectures in terms of accuracy, sensitivity, specificity, and computational efficiency. Our findings highlight the strengths and limitations of each approach, providing valuable insights for future research and clinical application.