<![CDATA[Optimization of resource allocation in fourth-generation cellular networks is critical to meet increasing demands for high data rates and low latency. The novelty of this research lies in the combination of network-spatial clustering with genetic algorithm-based physical tuning, which has not been jointly applied in the prior optimization of cellular networks. The clustering component partitions network zones based on spatial characteristics and traffic density, enabling localized parameter adjustment. The genetic algorithm performs physical tuning by iteratively selecting parameter sets that maximize network performance metrics. Experimental results demonstrate a significant enhancement in average data throughput, with observed increases of over twenty percent, and a reduction in latency by approximately twenty milliseconds compared to conventional tuning methods. These improvements translate into a more consistent user experience and better resource utilization under varying traffic conditions. The proposed approach also shows robustness across diverse urban scenarios, indicating its applicability to real-world deployments. By adapting to dynamic traffic patterns and environmental factors, the proposed solution ensures sustained network quality during peak demand and in challenging propagation environments. Future research will explore integration with machine learning"“based predictive models to further enhance tuning precision and proactive optimization. In conclusion, the hybrid network-spatial clustering and genetic algorithm"“based physical tuning method outperforms traditional optimization techniques by delivering higher performance gains and adaptability, offering a practical framework for enhancing fourth-generation network efficiency and laying the foundation for extending the methodology to emerging wireless standards.]]>
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