Low visibility is a critical factor affecting aviation and transportation safety, often leading to operational disruptions, delays, and potential hazards. Weather phenomena, such as fog, rain, and snow, significantly contribute to reducing visibility, making accurate prediction essential for mitigating risks. Conventional forecasting methods with time-series visibility and meteorological data often struggle with data imbalance and censored data issues, which impact forecasting accuracy, particularly in the low visibility range. In this paper, we propose a new approach by employing Censored Quantile Regression Neural Network and Light Gradient-Boosting Machine to forecast visibilities in the low and high visibility ranges and combining the forecast values by using a probabilistic classifier model built with Logistic Regression. We show the effectiveness of the proposed approach by performing experiments with two datasets of observed and forecast meteorological data from Japan and evaluating it in terms of forecasting errors and the accuracy of forecasting of low visibility. Experimental results suggest that our approach is well-suited to forecast low visibility with high accuracy up to 24 h ahead.