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There has been an increase in the implementation of Artificial Intelligence (AI) in the dairy industry for Milk Quality Analysis (MQA). However, traditional Machine Learning (ML) algorithms may not be effective due to non-linearity in milk spectral data and the requirement of pre-processing. Important features from the spectral data may be lost during the pre-processing stage, which is a severe problem. Deep Learning (DL) can help by eliminating the need for pre-processing, thereby avoiding the loss of information. Although traditional DL methods have been used in dairy farming applications, fewer studies indicate the use of DL for MQA. Therefore, there is a need to develop novel DL models for MQA to improve the classification accuracy for milk quality monitoring. This study proposes a Hybrid Blended Deep Learning (HyBDL) approach for better classification accuracy and lower prediction errors. The proposed …