<p>The rapid growth of IoT networks has introduced significant challenges in Optimizing routing and configuration parameters, particularly in dynamic and heterogeneous environments. Existing algorithms struggle with adaptability, scalability, and simultaneous multi-parameter optimization. The proposed Bacterial Foraging-Inspired Multi-Dimensional Optimization Algorithm for Internet of Things (BF-MDOA) utilizes bio-inspired mechanisms—chemotaxis, swarming, reproduction, and elimination-dispersal—to adaptively optimize routing paths and control parameters: energy consumption (χ), congestion (η), latency (γ), and packet loss (δ), based on network feedback. It initializes a bacterial population with randomized configurations and iteratively minimizes a weighted multi-objective fitness function. Chemotaxis directs gradient-based exploration, while the swarming phase adaptively tunes step sizes to reflect global network conditions. Reproduction reinforces high-performing solutions, and elimination-dispersal introduces diversity to avoid local optima. The proposed BF-MDOA has been evaluated across various mobility scenarios based on performance indicators to demonstrate the performance when compared to the existing optimization algorithms. The research findings validate that BF-MDOA effectively minimizes energy usage, packet loss, latency, congestion, extends network lifetime, and optimizes data transmission, making it suitable for dynamic IoT environments. These findings demonstrate that BF-MDOA significantly improves IoT network performance and efficiency.</p> Graphical Abstract <p></p>