Hyperspectral imaging (HSI) has gained prominence in various fields of science. In particular, it has spurred much interest in biomedical imaging especially cancer (such as skin, breast, oral, colon, pancreatic, and prostate) detecting applications. Of them, breast cancer (BC) is known to be the second-largest cause of mortality throughout the world. According to the Cancer Registry Program, over 1.3 million people in India are suffering from BC, and more recently, the numbers seem to be growing exponentially. Currently, no permanent cure for metastatic BC is reported; nevertheless, detecting it at an earlier stage and treating accordingly is shown to reduce its severity, i.e., increasing the survival rate. To effectively detect BC, several optical techniques including mammography, ultrasound imaging, computed tomography, positron emission tomography, and magnetic resonance imaging are widely used. Note that these methods have their own merits and demerits such as the false-negative results, usage of higher-energy radiation, and poor soft tissue contrast, to name a few. Therefore, to validate the imaging results, a biopsy (using surgical excisions) is often performed, which is painful, troublesome, and may cause discomfort for a longer period. For this reason, cancer detection via non-invasive imaging methods is highly sought. Techniques such as thermal imaging, photo-acoustic imaging, and, more recently, HSI are shown to be providing satisfactory results at the laboratory scale. This chapter comprehensively reviews the utilization of HSI technique for the detection of various stages of breast cancer. We also review the state-of-the-art deep learning frameworks that are employed for automated breast cancer detection.