Hyperspectral imaging (HSI) captures detailed spectral and spatial information, making it a versatile tool across various domains. This paper presents a Convolutional Neural Network (CNN)based method for vein detection from hyperspectral images of human hands. The proposed method applies a dimensionality reduction technique to the input HSI to extract its features and reduce its dimensionality. A CNN model is then trained and used to identify the location of vein pixels in the input image. Three dimensionality reduction methods, namely Principal Component Analysis (PCA), Folded-PCA (FPCA), and Inter-band Correlation and Clustering using the K-means clustering method (ICC_k-means), were used to reduce the dimensionality of the input image. A 3D-CNN model is then trained to detect the location of veins’ pixels in the input HSI. A 3D-CNN model was trained on dimensionality reduced HSI data to accurately identify the location of veins’ pixels in the input HSI. Experimental results were generated using the HyperVein dataset. The dataset images were randomly divided into training, validation, and test sets. Experimental results show that the proposed method using the ICC k-means dimensionality reduction technique achieves the highest accuracy, precision, recall, false positive rate, false negative rate, and receiver operating characteristics compared to when PCA and FPCA methods are used.