Railways are considered to be an environmentally friendly and efficient means of transport for people and goods with increasing importance in the transport policies of many countries. However, the infrastructure and the substantial demand for maintenance create additional costs for railway operators. To overcome outdated maintenance modes, implementation of new solutions, optimization of maintenance activities and resource utilization are required. Through a systematic literature review (SLR), this work evaluates new approaches toward implementing predictive maintenance in the railway domain. A comprehensive search, including the IEEE Xplore, Science Direct, and ACM Digital Library has been conducted, focusing on papers related to predictive maintenance and railway systems, published in peer-reviewed journals since 2016. The selected papers were analysed and grouped to allocate the research purposes as well as the considered assets, components, predicted defects, and maintenance conditions. Furthermore, the utilized predictive maintenance algorithms and their limitations are structured and evaluated. Analysis shows that a great variety of algorithms were used for either defect detection or the prediction of conditions of 20 different components, which are critical for the safety and availability of railway operations. The study shows that the proposed approaches were successfully tested and yielded great potential for predictive maintenance solutions. Researchers state to enhance proposed solutions within their future work, increasing accuracy, performance, and widening the area of application in the railway domain.