In image reconstruction, enhancing the quality and precision of reconstructed images is critical due to its applications in various fields, such as medical diagnosis and satellite imagery interpretation (Fessler in (2019). arXiv preprint arXiv:1903.03510). This paper focuses on using the Artificial Bee Colony (ABC) Optimization, an optimization algorithm based on swarm intelligence, to improve image reconstruction algorithms (Xu and Noo in Phys. Med. Biol. 67(7):07TR01, 2022). The aim is to evaluate how ABC Optimization can be applied to optimize the reconstruction of images affected by noise and incomplete data (Wei et al. in Appl. Sci. 10:3288, 2020). A systematic comparison is provided, demonstrating the performance of ABC Optimization relative to conventional and current techniques used in image reconstruction, such as Magnetic Resonance Imaging (MRI) reconstruction methods (Baskar et al. in International Conference on Big data and Cloud Computing. Springer Nature Singapore, Singapore, pp. 231–248, 2022). The study’s findings reveal that the ABC Optimization algorithm offers both higher image quality and time efficiency, showing promise for practical applications in medical imaging and remote sensing (Jun in Sci. Rep. 13:14,407, 2023). The experimental results, derived from case studies, illustrate that the ABC Optimization algorithm significantly reduces reconstruction error and preserves details more effectively than traditional methods (Bharathy et al. in SSRG Int. J. Comput. Sci. Eng.-Ing (SSRG-IJCSE) 6–10, 2017). This work delivers a comprehensive evaluation of the ABC Optimization algorithm’s effectiveness in image reconstruction and suggests avenues for future enhancement of this optimization approach (Wei et al. in Opt. Lasers Eng. 103:110–118, 2018).