This research article presents a novel approach to breast cancer diagnosis using deep convolutional generative adversarial networks (DCGANs) for generating synthetic mammograms. The primary goal is to address the scarcity of annotated mammogram data, which is a significant challenge in building reliable deep learning models for breast cancer detection. The study aims to produce synthetic mammograms that accurately replicate real-world patterns, enhancing the current dataset's diversity and clinical applicability. The proposed method involves training DCGANs on a benchmark dataset (DDSM) to generate synthetic images. The generated images are then evaluated for their similarity to real images using mean similarity measures and standard deviation analyses. Outliers are identified and removed using a threshold-based mechanism to improve the reliability of the synthetic dataset. Visual validation by expert radiologists further confirms the clinical accuracy and utility of the synthetic mammograms. The study demonstrates that the DCGAN-generated images are highly realistic and consistent with real mammograms, as evidenced by the mean similarity assessments and statistical validation. The results show that the synthetic images closely match the real data distribution, with distinct and coherent clusters of similar data points after outlier removal. The effectiveness of the proposed method is further validated by human experts, who achieved an average accuracy of 68% in distinguishing synthetic from real images. The research concludes that DCGANs can effectively generate high-quality synthetic mammograms, enhancing the diagnostic precision of deep learning models for breast cancer detection. Future work will involve testing the method on more datasets and exploring different GAN architectures.This research article presents a novel approach to breast cancer diagnosis using deep convolutional generative adversarial networks (DCGANs) for generating synthetic mammograms. The primary goal is to address the scarcity of annotated mammogram data, which is a significant challenge in building reliable deep learning models for breast cancer detection. The study aims to produce synthetic mammograms that accurately replicate real-world patterns, enhancing the current dataset's diversity and clinical applicability. The proposed method involves training DCGANs on a benchmark dataset (DDSM) to generate synthetic images. The generated images are then evaluated for their similarity to real images using mean similarity measures and standard deviation analyses. Outliers are identified and removed using a threshold-based mechanism to improve the reliability of the synthetic dataset. Visual validation by expert radiologists further confirms the clinical accuracy and utility of the synthetic mammograms. The study demonstrates that the DCGAN-generated images are highly realistic and consistent with real mammograms, as evidenced by the mean similarity assessments and statistical validation. The results show that the synthetic images closely match the real data distribution, with distinct and coherent clusters of similar data points after outlier removal. The effectiveness of the proposed method is further validated by human experts, who achieved an average accuracy of 68% in distinguishing synthetic from real images. The research concludes that DCGANs can effectively generate high-quality synthetic mammograms, enhancing the diagnostic precision of deep learning models for breast cancer detection. Future work will involve testing the method on more datasets and exploring different GAN architectures.