Brown rot, caused by Monilinia spp., is one of the most important postharvest diseases of stone fruits worldwide. Brown rot resistance in peach is a polygenic trait controlled by multiple genes with a small effect. In this study, we assessed the feasibility of genomic prediction (GP) for brown rot resistance in peach using eight contrasting methods (GBLUP, rrBLUP, BayesA, BayesB, BayesC, Bayesian Ridge Regression, Bayesian Lasso and RKHS). A testing panel of 38 cultivars/advanced selections and 288 F1 individuals from 27 pedigree-related breeding families with 'Bolinha' and/or 'Contender' or almond source of resistance was phenotyped over six seasons (2015 to 2020). GP models outperformed MAS models under five-fold cross validation, and low to moderate predictive accuracy (PA) was achieved by fitting GP model for wounded (W) (0.092−0.449) and low PA for non-wounded (NW) disease severity index (0.129−0.295). An alternative cross validation approach using disease severity index recorded in lab to predict field disease incidence (FDI) in unphenotyped accessions revealed moderate correlation (0.548−0.553). Genomic predicted breeding value distinguished accessions with low FDI from those with high FDI. The results presented here demonstrated feasibility of incorporating GP in peach breeding.