The classification of brain tumors is a critical task in clinical practice. In recent years, the use of biochemical sensors combined with artificial intelligence techniques has emerged as a promising approach for brain tumor classification. This paper presents a review of recent research on brain tumor classification using biochemical sensors and artificial intelligence. Various techniques, such as machine learning algorithms, deep learning, and feature extraction methods, have been employed in these studies. The integration of multiple imaging modalities, such as magnetic resonance imaging (MRI) and mass spectrometry, has also been explored to improve classification accuracy. Several studies have reported high classification accuracies using these methods, which demonstrate the potential of biochemical sensors and artificial intelligence in brain tumor classification. However, further research is required to validate these approaches on larger datasets and in clinical settings.