Brain tumor refers to the abnormal enhancement of a collection of tissues or cells within the brain, occurring when brain cells grow improperly. Brain Tumors have currently been recognized as a major cause of mortality worldwide. As the most fatal category of cancer, brain tumors make detection and treatment critical for protecting lives. Single-Photon Emission Computed Tomography (SPECT) provides important imaging insights for detecting and evaluating brain cancer. However, variations in medical scenarios and imaging techniques make it difficult to improve models that are universally applicable across domains. This research suggests a novel framework for improving SPECT-based brain tumor diagnosis by combining Domain Adversarial Neural Networks (DANN) with Model Agnostic Meta Learning (MAML) and anisotropic diffusion filtering. The anisotropic diffusion filter reduces noise while maintaining tumor relevant structures, thereby preserving critical spatial properties for analysis. DANN supports domain adaptation by learning domain-invariant representations, allowing the model to perform consistently across different datasets. MAML enhances the model’s capability to adapt to new, unfamiliar domains with insufficient data, hence boosting its therapeutic value. Experimental results of the proposed DANN-MAML model with anisotropic diffusion filtering over diverse SPECT datasets outperformed existing strategies regarding both accuracy and flexibility, making it a promising tool for brain tumor identification.