Software requirements Change Impact Analysis (CIA) is a pivotal process in requirements engineering (RE) since changes to requirements are inevitable. When a requirement change is requested, its impact on all software artefacts has to be investigated to accept or reject the request. Manually performed CIA in large-scale software development is time-consuming and error-prone so, automating this analysis can improve the process of requirements change management. The main goal of this research is to apply a combination of Machine Learning (ML) and Natural Language Processing (NLP) based approaches to develop a prediction model for forecasting the requirement change impact on other requirements in the specification document. The proposed prediction model will be evaluated using appropriate datasets for accuracy and performance. The resulting tool will support project managers to perform automated change impact analysis and make informed decisions on the acceptance or rejection of requirement change requests.