In recent years reducing the automobile HVAC (Heating Ventilation and automobile conditioning) noise inside the vehicle cabin is one of the main criterions for all OEMs to provide comfort level to the passengers. The primary function of the HVAC is to deliver more air to the cabin with less noise generation for various blower speeds. Designing the optimum HVAC with less noise is one of the major challenges for all automotive manufacturers and HVAC suppliers. During the design stage, physical parts are not available and hence the simulation technique helps to evaluate the noise level of HVAC. In this study, a computational 1D (one dimensional) analysis is carried out to compute the airflow noise originated from the HVAC unit and propagated to the passenger cabin. Modeling has been done using unigraphics and the analysis is carried out using the commercial 1D software GT suite. The inner volume of the 3D HVAC model comprising of blower fan, evaporator, heater, housing and dampers are extracted and discretized in to 1D model using the GT suite volume extraction technique. The inputs for the analysis are the airflow at HVAC Inlet, blower speed, fan performance characteristics and the pressure drop characteristics of evaporator, filter and heater core. Imposed mass flow rate method is used to predict the airflow noise generated by flow through the HVAC. The result predicted is the sound pressure level in dBA measured at 50 cm away from HVAC outlet in defrost and panel mode for different air flow rates covering a frequency range of 0Hz to 5000 Hz. Bench level test is done by placing the HVAC unit in the anechoic chamber with the different air flow rates and the sound pressure levels are measured using a microphone. Simulation results are compared with the bench test results and the error is within 7 %. The validated model can be used to assess the HVAC noise during early design stages of the program and design can be optimized based on the simulation results. The above 1D methodology has an edge compared to 3D analysis as the simulation is faster and the results are well correlated. This 1D model significantly aids in reducing the physical tests and time required to predict the noise level.