The current study explores the usage of Recurrent type Neural Networks (RNNs) for the early identification and diagnosis of breast cancer by analysing genetic based data and clinical records. Traditional diagnostic methodology often rely on imaging, but recent advancements in deep learning allows for more effective usage of complex sequential data, lime patient past medical histories and genetic sequences. RNNs, with their capability to process time-dependent data, offers a promising solution for understanding the underlying genetic related markers and risk factors associated with breast cancer. By merging these diverse data sources, the model aims in identifying patterns that can improve diagnostic accuracy rate and assist in early identification. The research demonstrates how Rn -depending approaches can enhances decision-making for clinicians by providing deeper insights into patient-specific health-risks. This innovative approach underscores the potential of using deep learning techniques to revolutionized breast cancer diagnosis, potentially leading to more personalized and timely medical treatment strategies.