Breast cancer remains a leading cause of mortality among women worldwide, highlighting the critical need for effective early detection methods. DNA methylation, involving variations in methylation levels, serves as a significant biomarker for identifying cancerous changes at an early stage. In 2018 alone, approximately 40,920 women lost their lives to breast cancer, emphasizing the urgency of improving diagnostic techniques. Recent advancements in technology have enabled the development of more precise and timely prediction models for such conditions. Among these advancements, machine learning has emerged as a transformative tool, capable of analyzing complex physical and behavioral data to predict diseases with high accuracy. In the context of early breast cancer detection, a cascaded approach utilizing advanced machine learning techniques has shown great promise. This study introduces a multi-step methodology that begins with the Standard Deviation Threshold based Differential Mean Feature Selection (DMFS) technique. By selecting the most informative features from the input data, this method optimizes prediction accuracy through a threshold set at the standard deviation of weight vectors. Following feature selection, Principal Component Analysis (PCA) and Neighborhood Component Analysis (NCA) are applied to further refine these features, enhancing the model’s ability to distinguish between cancerous and non-cancerous states. This paper presents a comprehensive analysis of these advanced machine learning techniques and their application to DNA methylation data for early breast cancer detection. The findings demonstrate significant improvements in prediction accuracy, underscoring the potential of these methods to contribute to more effective and timely breast cancer diagnosis, ultimately aiming to reduce mortality rates associated with the disease.