The study employs a novel empirical approach that synergises recent advancements in Machine Learning, new data sets, and Spatial Data Science techniques to systematically examine gentrification trends in London. The research team used Principal Component Analysis (PCA), K-Means clustering, and in-depth spatial analysis to identify neighborhoods in London that have undergone recent gentrification, explore which neighborhoods are likely to be next in line, and present data code and novel interactive visualisations as a comprehensive tool for supporting policy and decision making in the city. The study found that gentrifying LSOAs were predominantly located in traditionally richer upscale West London boroughs and East London, affecting over half-a-million residents, with super-gentrification, marginal gentrification, and mainstream gentrification simultaneously taking place in London and unlikely to diminish in the near future.