Telecommunications networks generate extensive performance and environmental telemetry, yet most LTE and 5G-NR deployments still rely on static, manually engineered configurations. This limits adaptability in rural, nomadic, and bandwidth-constrained environments where traffic distributions, propagation characteristics, and user behavior fluctuate rapidly. Artificial Intelligence (AI), more specifically Machine Learning (ML) models, provide new opportunities to transition Radio Access Networks (RANs) from rigid, rule-based systems toward adaptive, self-optimizing infrastructures that can respond autonomously to these dynamics. This paper proposes a practical architecture incorporating AI-assisted planning, reinforcement-learning-based RAN optimization, real-time telemetry analytics, and digital-twin-based validation. In parallel, the paper addresses the challenge of delivering embodied-AI healthcare services, educational tools, and large language model (LLM) applications to communities with insufficient backhaul for cloud computing. We introduce an edge-hosted execution model in which applications run directly on LTE/5G-NR base stations using containers, reducing latency and bandwidth consumption while improving resilience. Together, these contributions demonstrate how AI can enhance network performance, reduce operational overhead, and expand access to advanced digital services, aligning with broader goals of sustainable and inclusive network development.

翻译：电信网络生成大量性能与环境遥测数据，然而大多数LTE和5G-NR部署仍依赖静态的人工工程配置。这在农村、游牧及带宽受限环境中限制了适应性——这些场景中的流量分布、传播特性和用户行为往往快速波动。人工智能（AI），特别是机器学习（ML）模型，为无线接入网络（RAN）从僵化的基于规则系统向自适应、自优化的基础设施转型提供了新机遇，使其能够自主响应这些动态变化。本文提出一种融合AI辅助规划、基于强化学习的RAN优化、实时遥测分析与数字孪生验证的实用架构。同时，本文探讨了如何向回传带宽不足以支撑云计算的社区提供具身AI医疗服务、教育工具及大语言模型（LLM）应用的挑战。我们引入一种边缘托管执行模型，该模型通过容器技术使应用直接在LTE/5G-NR基站上运行，从而降低时延与带宽消耗，并提升系统韧性。这些成果共同论证了AI如何提升网络性能、降低运营开销并扩大先进数字服务的覆盖范围，与可持续及包容性网络发展的宏观目标相契合。