We propose an approach leveraging the power of self-organizing maps (SOMs) in conjunction with a multiscale local image fitting (LIF) level-set function to enhance the capabilities of the region-based active contour model (ACM). In addition, we employ the lattice Boltzmann method (LBM) to ensure efficient convergence during the segmentation process. The SOM learns the underlying patterns and structures of both the background region and the object of interest region in an image, allowing for more accurate and robust segmentation results. Our multiscale LIF level-set approach influences image-specific fitting criteria into the energy functional, considering the features extracted by the SOM. Finally, the LBM is utilized to solve the level set equation and evolve the contour, allowing for a faster contour evolution. To evaluate the effectiveness of our approach, we performed our experiments on the challenging Pascal Visual Object Classes Challenge 2012 dataset. This dataset consists of images containing objects with diverse characteristics, such as illumination variations, shadows, occlusions, scale changes, and cluttered backgrounds. Our experimental results highlight the efficiency and robustness of our proposed method in achieving accurate segmentation. In terms of accuracy, our approach outperforms state-of-the-art learning-based ACMs, reaching a precision value of up to 93%. Moreover, our approach also demonstrates improvements in terms of computation time, leading to a reduction in computational time of 76% compared with the state-of-the-art methods. By integrating SOMs and the LBM, we enhance the efficiency of the segmentation process. This enables us to achieve accurate segmentation within reasonable time frames, making our method practical for real-world applications. Furthermore, we conducted experiments on medical imagery and thermal imagery, which yielded precise results.