Breast cancer is the most common cancer for a woman to develop in her lifetime. By detecting breast cancer at an early stage, the symptoms can be easier to manage and the patient should have the best chance of survival. The current gold standard for breast cancer detection is a mammogram, followed by a biopsy and histopathology. This is effective but can also be expensive and invasive. A promising addition to the diagnostic pathway uses vibrational spectroscopy which utilises non-elastic interactions between light and tissue. Raman spectroscopy has been used widely in industry and research: it is a non-invasive and chemically specific technique. This spectroscopic technique has been proven to be applicable to the detection of microcalcifications in breast tissue to aid in diagnosing breast cancer and potentially reducing the number of biopsies required. This thesis involves the development of algorithms to model Raman scattering in biological tissues to aid in the improvement of breast cancer detection. The technique used is the numerical modelling method Monte Carlo Radiative Transport (MCRT) to effectively simulate the transport of light through turbid media. There is a need for a fast and flexible code capable of modelling a variety of Raman source materials, tissue types and shapes, input laser beams and detectors. This rapid simulation of light transport through breast tissue can provide more information and insight to complement the practical measurements and analysis of experimental work, which can be used to improve future experiments and probes. By implementing physically correct Raman scattering into a fast and powerful code, and utilising work from the field to estimate the optical properties of tissues, simulations to supplement experimental work and predict potential clinical results are performed and analysed.