<p>Brain tumors are ranked highly among the leading causes of cancer-related fatalities. Precise segmentation and quantitative assessment of brain tumors are crucial for effective diagnosis and treatment planning. However, manual segmentation is often laborious, challenging, and prone to errors, necessitating the creation of a fully automated brain tumor segmentation approach. This article introduces “3D AIR-UNet,” an end-to-end architecture aiming to automate the segmentation of brain tumors from MRI data. The presented model employs an encoder–decoder architecture, with carefully constructed inception–residual units replacing the usual convolution layers used in UNet. The inception–residual block combines the advantages of inception modules and residual connections to provide a powerful feature extraction mechanism. It captures extensive multi-scale information by combining different filter sizes. This block’s design is effective at handling complex 3D data patterns, making it a vital component of sophisticated neural network architecture. Moreover, an attention mechanism further boosts the capability of the model to differentiate between tumor and non-tumor regions, leading to improved localization and contextual understanding. Additionally, skip connections are employed between the encoder and decoder at each level to speed up the training process. The proposed 3D AIR-UNet architecture demonstrated encouraging outcomes, attaining dice scores of 0.9218 for the whole tumor, 0.9019 for the tumor core, and 0.8788 for the enhancing tumor when evaluated on the BraTS 2020 dataset. Comparative analysis with contemporary methods suggests that 3D AIR-UNet notably enhances the segmentation accuracy of brain tumor subregions.</p>