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Biomass can be converted to bio-fuels and bio-chemicals via many thermo-chemical conversion platforms such as hydrothermal liquefaction (HTL) which occurs at high temperature and pressure conditions. HTL can be used for converting wet or high moisture content biomass to biocrude oils effectively. There are wide variety of relevant parameters affecting the system of biomass HTL, for example, operating conditions, biomass characteristics, solvent properties, or catalyst identities used. This brings about the difficulty in making highly accurate prediction of biocrude oil yields from biomass HTL. In this work, a gradient tree boosting machine learning (GTB-ML) model with principal component analysis (PCA) was applied to predict the yields of biocrude oils from biomass HTL, using 15 input variables from biomass characteristics, operating conditions, and solvent properties. PCA is a statistical method to find correlations in multivariate data, and it can be used to specify highly influential variables in input datasets for ML models. PCA results confirmed that input variables from biomass characteristics were as important as those from HTL operating conditions. The GTB-ML developed from principal components 1 to 8, representing about 90% of the whole original dataset can be used to predict the yields of bio-crude oils from biomass HTL at acceptable accuracy with correlation coefficient, R2=0.8and root mean square error = 0.005.