Early design choices in building shape and fenestration significantly influ- ence the yearly daylight performance of office buildings. Annual daylight performance must be analyzed at the conceptual design stage to support building form and fenestration design decisions. However, the simulation modeling and daylight calculations necessary for the annual daylight fore- cast are extraordinarily time-consuming, which negatively influences its early design viability. Machine learning-based methods that experimentally learn from simulation-derived data have been implemented to decrease the time of daylight simulations. We concentrate on the visual comfort of working en- vironments. This particular sort of area demands more visual comfort than others. Four machine-learning methods are compared concerning their appli- cability in spatial daylight autonomy, annual sunlight exposure, and spatially disturbing glare. This research proposes a machine learning-based modeling strategy for predicting yearly daylight performance early in the design stage. The developed prediction model results for the sDA(Spatial Daylight Auton- omy), ASE (Annual Sunlight exposure), and sDG(Spatial Disturbing Glare) settings. After comparing the models for each output the best chosen model attained R2 scores of 0.85, 0.65, and 0.26 and MAE scores of 3.33, 22.5, and 22.16, respectively.