Breast Cancer (BC) is one among the most critical diseases affecting women across the world mostly between the ages 35-55 and is growing every year in a capricious rate. By identifying the disease in its early stages, we can control the occurrence of BC. The manual approach used by radiologists has failed since they are similar in the appearance of micro-calcifications, and segmentation of breast images are complicated process. Hence there is a need of automated systems for detection of the disease in early-stage which helps to assist radiologists for diagnosing the disease in a precise manner and make necessary decisions for patients' treatment in the future. For classifying and predicting the disease, swarm intelligence (SI) plays a major role in terms of obtaining an optimal solution and reducing its training time. This paper presents study on review and analyses the performance of breast cancer disease predictive modelling using swarm intelligence. Also, it recommends the solution for forthcoming exertion to predict the location of the tumour in an efficient manner.