Breast cancer is the most common cancer, which resulted in the death of 700,000 people around the world in 2020. Breast cancer is among the top causes of fatalities related to cancer in females and breast cancer remains a substantial public health challenge, marked by a rising prevalence. Radiologists commonly use mammogram images to detect breast tumors in their early stages. Mammography images contain a lot of information about not only the mammary glands but also the skin, adipose tissue, and stroma, which may reflect the risk of developing breast cancer. Accurate early detection is paramount for effective treatment and improved patient outcomes in breast cancer. X-ray mammography is currently considered the golden standard method for breast cancer screening; however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This research discusses how deep learning-assisted X-ray mammography can improve the accuracy of breast cancer screening. While the potential benefits are evident, it is critical to overcome the limitations of deploying this technology in clinical settings. This paper presents a computer aided diagnosis (CAD)-based hybrid model combining convolutional neural net-works (CNN) with learning machine methods to enhance breast cancer detection, segmentation, feature extraction, and classification where we used hybrid model of CNN-SVM with accuracy 98.79% and second hybrid model CNN-KNN with accuracy 96.49%. #CSOC1120.