In this study, we address white balance calibration in hyperspectral images using statistical techniques. We employed the Senop HSC-2 hyperspectral camera, capable of capturing high-resolution images (1024 × 1024 pixels) across up to 1,000 spectral bands within the 450 to 800 nm range. Each pixel has a 12-bit depth, resulting in a data cube size of approximately 41,943,040 bytes. Exposure times were adjusted between 10 ms and 25 ms to optimize data quality under varying lighting conditions. The camera has a white reference part next to the image area while taking pictures. This is for later adjustments. A spot of 35 × 35 pixels, which equals 1,225 data points per color band, was chosen for checking. The data were lined up one after the other, and complex math methods were used to find best fix values for each spectral band, with a goal to correct color mistakes and boost accuracy. The findings show that using these math techniques allows better white balance fixing in hyperspectral imaging. This leads to better data quality and helps in many scientific and industrial uses.