The lack of awareness of blind spots in vehicle transport results in more deaths nowadays. To address this issue, the multi-obstacle detection and measurement of the depth of the nearing vehicle, height, and width is necessary. In recent years, Fuzzy logic is being used to access smart decision-making for control actions. To handle the specific task efficiently, ambiguous and imprecise linguistic data is required. In this context, a non-linear intelligent fuzzy decision-making system has been proposed to estimate blind spots. An inference engine, a defuzzification interface to identify the blind spot both day and night, and a fuzzy rule-base are included. Shadows and edges can be used as linguistic parameters to identify vehicles in the daytime. The lamps are elevated higher than the air dams to avoid casting a shadow under the car at night. One in-sourcing vehicle and three out-sourcing vehicles are tested to determine the driver’s blind spot and a more comfortable driver’s seat and a rear-view mirror using the proposed system. A fuzzy matrix with a triangular number obtained from the crisp matrix is used to alert the driver of the likelihood of a collision using LEDs or buzzers.