Hyperspectral Remote Sensing (HRS) is crucial for detecting and mapping the exact position of minerals by analyzing their spectral characteristics in various formations of rock within the Visible Near Infra-Red (VNIR) and Short-Wave Infra-Red (SWIR) range of the electromagnetic radiation spectrum using spaceborne and airborne data. Airborne data availability facilitates the straightforward identification of economically prosperous mineral rich areas. The research has been conducted using the hyperspectral dataset from the Airborne Visible Infrared Imaging Spectrometer Next Generation (A VIRIS-NG) in the Pichavaram area of Chidambaram town, located in the Cuddalore district of Tamil Nadu, India. This study aims to detect minerals by analyzing the spectral reflectance curve of images in conjunction with the USGS Laboratory spectra of minerals included in the ENVI library. This research seeks to identify abundant mineral deposits like vermiculite, antigorite, diopside, rectorite, and ammonio jarosite along the coastal areas. The Spectral Angle Mapper (SAM) and Spectral Feature Fitting (SFF) algorithms are utilized for mapping and analysis—the investigation aimed to identify economically valuable areas with mineral wealth using A VIRIS-NG data. In future work, machine learning models (MLMs) using supervised classifiers can improve the availability and accurate identification of minerals.