Cell surface proteins are of great biomedical importance as demonstrated by the fact that 66% of approved human drugs listed in the DrugBank database target a cell surface protein. Despite this biomedical relevance, there has been no comprehensive assessment of the human surfaceome, and only a fraction of the predicted 5,000 human transmembrane proteins have been shown to be located at the plasma membrane. To enable analysis of the human surfaceome, we developed the surfaceome predictor SURFY, based on machine learning. As a training set, we used experimentally verified high-confidence cell surface proteins from the Cell Surface Protein Atlas (CSPA) and trained a random forest classifier on 131 features per protein and specifically, per topological domain. SURFY was used to predict a human surfaceome of 2,886 proteins with an accuracy of 93.5%, which shows excellent overlap with known cell surface protein classes (i.e., receptors). In deposited mRNA data, we found that between 543 to 1,100 surfaceome genes were expressed in cancer cell lines and maximally 1,700 surfaceome genes were expressed in embryonic stem cells and derivative lines. Thus, the surfaceome diversity depends on cell type and appears to be more dynamic than the non-surface proteome. To make the predicted surfaceome readily accessible to the research community, we provide visualization tools for intuitive interrogation (wlab.ethz.ch/surfaceome). The in silico surfaceome enables the filtering of data generated by multi-omics screens and supports the elucidation of the surfaceome nanoscale organization using proximity-based tagging strategies. Proximity Radical Tagging (PRT) and LUX-MS technology are two new chemical proteomics-based strategies which enable the identification of ligand-receptor interactions, but also the elucidation of lateral/cis interactions within the surfaceome. Proximity-tagging by LUX-MS and PRT provide new molecular spatial relationship information which could be exploited for drug targeting.