Breast　cancer　is　one　of　the　worst　illnesses,　with　a　higher　fatality　rate　among　women　globally.　Breast　cancer　detection　needs　accurate　mammography　interpretation　and　analysis,　which　is　challenging　for　radiologists　owing　to　the　intricate　anatomy　of　the　breast　and　low　image　quality.　Advances　in　deep　learning-based　models　have　significantly　improved　breast　lesions＇　detection,　localization,　risk　assessment,　and　categorization.　This　study　proposes　a　novel　deep　learning-based　convolutional　neural　network　(ConvNet)　that　significantly　reduces　human　error　in　diagnosing　breast　malignancy　tissues.　Our　methodology　is　most　effective　in　eliciting　task-specific　features,　as　feature　learning　is　coupled　with　classification　tasks　to　achieve　higher　performance　in　automatically　classifying　the　suspicious　regions　in　mammograms　as　benign　and　malignant.　To　evaluate　the　model＇s　validity,　322　raw　mammogram　images　from　Mammographic　Image　Analysis　Society　(MIAS)　and　580　from　Private　datasets　were　obtained　to　extract　in-depth　features,　the　intensity　of　information,　and　the　high　likelihood　of　malignancy.　Both　datasets　are　magnificently　improved　through　preprocessing,　synthetic　data　augmentation,　and　transfer　learning　techniques　to　attain　the　distinctive　combination　of　breast　tumors.　The　experimental　findings　indicate　that　the　proposed　approach　achieved　remarkable　training　accuracy　of　0.98,　test　accuracy　of　0.97,　high　sensitivity　of　0.99,　and　an　AUC　of　0.99　in　classifying　breast　masses　on　mammograms.　The　developed　model　achieved　promising　performance　that　helps　the　clinician　in　the　speedy　computation　of　mammography,　breast　masses　diagnosis,　treatment　planning,　and　follow-up　of　disease　progression.　Moreover,　it　has　the　immense　potential　over　retrospective　approaches　in　consistency　feature　extraction　and　precise　lesions　classification.