Soil analysis involves evaluating the physical, chemical, and biological properties of soil samples, encompassing factors such as composition, nutrient content, structure, pH levels, microbial activity, and organic matter. Hyperspectral imaging (HSI) in conjunction with machine learning (ML) techniques represents an emerging research trend for identifying soil properties. While numerous studies focus on agriculture comprehensively, there is a notable deficiency in research specifically addressing soil analysis. This study reviews recent research exploring the integration of HSI with ML techniques for soil analysis, examining key aspects, including soil properties, datasets, HSI technologies, data pre-processing methods, ML techniques, and outlining future directions. The reviews highlight a predominant focus on the chemical properties of soil compared to its physical and biological attributes. The Savitzky–Golay (SG) filter emerges as a widely explored method for denoising data, while principal component analysis (PCA) is frequently employed for dimensionality reduction. Regarding machine learning techniques, convolutional neural networks (CNN) and support vector machines (SVM) are extensively utilized. Despite considerable attention on hyperspectral imaging with machine learning for soil analysis, notable research gaps exist that warrant further exploration.