This work focuses on the acquisition and analysis of the multispectral behavior of mammary tissue representing glandular tissue, adipose tissue, and/or a tumor, incorporating a perfusion system, in order to characterize the constructed structure using image-processing algorithms applied to the acquired data. A synthetic model was fabricated from organic materials in relative proportions that simulate the two typical breast tissues—glandular and adipose—together with a material that presumably represents the tumor. The specimens consist of an outer ring (glandular or adipose tissue) containing the tumor at its center, and the entire system is traversed by two tubes carrying a fluid that mimics blood circulation. Two lamps irradiate the structure to thermally stimulate the surface, thereby enabling the simultaneous acquisition of thermographic images and spectral images in the visible region, which were subsequently processed using Python. An experimental and computational procedure was developed to acquire and process multispectral images of the simulated tissues. The algorithm implemented for thermographic acquisition enabled the identification of two differentiated regions during thermal wave propagation by employing the K-means algorithm. In addition, the temporal evolution during the cooling process allowed the cooling constant to be identified as a characteristic parameter of the process. Regarding the acquisition of multispectral images in the visible range (MSI), it was possible to differentiate diffuse reflectance among the different simulated tissues and the vascularization system. The applied methodology enabled a comprehensive analysis that facilitated the understanding of thermal responses as a function of time in the implemented experimental model. The algorithm allowed the segmentation of regions of interest, delineating regions associated with distinctive spectral contrasts. The results are consistent with the mathematical models studied, enabling an adequate differentiation among the tissues.