Automatic Speech Recognition (ASR) systems have become essential tools for the communication in natural human-computer communication across various languages. The growing need for effective communication in public and private sector has increased the need of Natural Language Processing. Gujarat is one of the prominent state in India where most of the official communication takes place in Gujarati language. ASR for Gujarati language has its own importance due to the high usage of Gujarati language. This paper, highlights the emergence of ASR for Gujarati language from the early days of creating ASR commercially to more modern advancement approaches such as deep learning, end-to-end (E2E), and various other methods. Traditional ASR systems utilized statistical methods, most commonly Hidden Markov Models (HMM) and Gaussian Mixture Model (GMM) which often reported low accuracy. With the emergence of deep learning models, including CNN, BiLSTM, and transformer-based models such as Wav2Vec 2.0 and XLSR-53, the recognition accuracy has significantly increased, especially in noisy and spontaneous speech contexts. The paper also provide the comparative study of the work done by various researchers in the field of Gujarati ASR, highlighting that the use of spell correctors as well as hybrid feature extraction methods have reduced phonetic ambiguities and diacritic errors in Gujarati language. The study strongly emphasizes the need for morphological analysis and contextual modeling to adequately address the complexity in the homophones and diacritics of Gujarati language. The paper also reviews the various methods applied to Gujarati ASR and their reported Word Error Rates.