Breast cancer remains the most frequently diagnosed malignancy among women worldwide and accurate histological grading is essential for prognosis and therapeutic decision-making. Conventional grading systems rely on subjective microscopic evaluation of nuclear pleomorphism, which is prone to interobserver variability. Automated nuclear morphometry has emerged as a quantitative digital pathology approach that objectively measures nuclear characteristics such as area, perimeter, circularity, texture, chromatin distribution, and nuclear-cytoplasmic ratio. Advances in artificial intelligence (AI), machine learning, whole-slide imaging, and deep learning have significantly improved the accuracy and reproducibility of breast cancer grading. Automated image analysis enables high-throughput assessment of thousands of tumor nuclei, facilitating standardized grading while reducing observer bias. This review summarizes current methodologies, morphometric parameters, clinical applications, technological advancements, advantages, limitations, and future perspectives of automated nuclear morphometry in breast cancer grading.