In recent years, the rapid proliferation of smart mobile devices has significantly expanded the scale of wireless communication networks. The exponentially growing demand for mobile data traffic has led to more severe energy consumption issues and resource wastage in power allocation. This paper aims to address key challenges under the development trends of intelligent, green, energy-efficient, and diversified wireless communication and network architectures. These challenges include limited throughput and bandwidth in traditional cellular network power allocation methods, severe inter-cell interference, high communication time costs, low algorithmic efficiency, and unbalanced traffic distribution across network cells. By simulating a 4G cellular network base station communication environment on the Vienna LTE simulation platform, the study analyzes the shortcomings and potential improvements of existing power allocation algorithms. It investigates common intelligent algorithms and reinforcement learning strategies, explores their algorithmic motivations, and selects suitable algorithms for communication environments and power allocation tasks. Parameter tuning and application adaptation are conducted on eligible intelligent algorithms or reinforcement learning strategies to ensure global optimization. Ultimately, the simulation results demonstrate improved performance metrics—such as throughput—of the cellular communication system. The study also conducts an optimization rate and time cost analysis for various power allocation algorithms, achieving power adjustment across multiple base stations in multi-cell, multi-user scenarios. The proposed approach ensures both practical social value and efficiency optimization, thereby accomplishing the research and implementation of power allocation algorithms for cellular networks.