The fusion of multispectral (MSI) and hyperspectral images (HSI) has been acknowledged as a promising method for performing HSI-MSI fusion, which is also termed to be an essential part for precise recognition and cataloging of the underlying materials. The goal of HSI-MSI fusion is to fuse the low-resolution HSI with the high-resolution MSI to produce high-resolution images. The HSI-MSI fusion is employed as an imperative part for addressing the problems of image processing. However, the HSI-MSI fusion needs high resolution image to perform precise analysis and decision making, which is the major issue due to deficiencies of current satellite sensors that seems impossible to attain quality images. Numerous techniques are devised in the prior works that employed image fusion using HSI and MSI. This paper presents complete survey of 80 papers using HSI-MSI fusion methodologies, which involves several techniques like Pansharpening methods, Subspace-based methods, Artificial intelligence-based methods, Deep learning methods and Hybrid models. In addition, thorough investigation are performed based on the year of publication, adapted methodology, datasets used, implementation tool, evaluation metrics, and values of evaluation metrics. Finally, the issues of existing methods and the research gaps considering conventional HSI-MSI fusion schemes are elaborated to obtain improved contribution for devising significant HSI-MSI fusion techniques.