There is a growing interest from breeders and producers in the development of single seed methodologies for quality analysis. Chickpea (Cicer arietinum) is one of the most important food legume crops with 13 million tons of global production and is consumed worldwide for its excellent carbohydrate, protein, minerals, dietary fiber, and vitamin content. This study explores the use of single kernel NIR (SKNIR) spectroscopy for rapid and nondestructive detection of chickpea seed composition and weight. Spectra were acquired from the diverse chickpea accessions with a single-seed based NIR spectrometer. Next, we developed partial least squares (PLS) regression models based on wet-lab reference data and spectra for protein, oil, and weight. For the protein model, the values for R2 and SEP were 0.85 and 1.56, respectively. For the oil model, the values for R2 and SEP were 0.80 and 10.37, respectively. For the weight model, the values for R2 and SEP were 0.835 and 0.06, respectively. The SKNIR spectroscopy technique was efficient in further predicting the oil and protein content of unseen cultivars. SKNIR spectroscopy coupled with PLS regression has revealed the feasibility of protein, oil, and weight prediction in single chickpea seeds. This study demonstrates the potential of SKNIR spectroscopy technique that should enable rapid screening of large number of chickpea genotypes. This research provides crop breeders and producers with a valuable tool for selecting chickpea varieties for their quality content.