In this paper, our primary objective is to present an AI-based model designed for the detection of breast cancer through the analysis of microwave data. The data utilized in this study were collected by MammoWave scanner, with the magnitude of the $\mathbf{S 2 1}$ complex number serving as the raw data input. We employed an Auto Encoder-Decoder for feature extraction, followed by the utilization of a Probabilistic Neural Network for the detection of malignant lesions. The results obtained indicate that although there is an imbalance in the distribution between healthy and non-healthy classes, our proposed method demonstrates the ability to address this challenge effectively through fine-tuning. Our approach achieves an approximate $60 \%$ rate for accuracy, sensitivity, and specificity, showcasing its promising potential in breast cancer detection.