The increasing demands in 5G networks for ultra-reliable low-latency communication require advanced techniques for decoding polar codes with balanced throughput, energy efficiency, and error resilience sets. Although the decoding algorithms of polar codes for existing techniques are backed by theory, they usually operate with high latencies, high energy consumption, and limited adaptability to dynamic channels and hardware conditions, challenges that limit their applicability in real-time edge-based 5G applications. To ameliorate these limitations, this study lays down a complete performance analysis and an algorithmic framework incorporating five new techniques for the optimization of polar code processing to 5G system constraints. Channel-Conditioned Adaptive Decoding Tree Pruning (C-ADTP) prunes the decoding tree based on real-time Channel State Information (CSI) and Quality-of-Service (QoS) constraints, which translates into 25–35% reduction in latency, and 18–22% energy savings with negligible degradation of the FER. The Sparse Instruction-Level Polar Vectorization Engine (SIL-PVE) offers acceleration through sparse bitwise vectorization in conjunction with architectures such as SIMD/VLIW and exhibits a 1.8×–2.4× increase in throughput with a 30% reduction in decoding time. To further enhance adaptability to mobile environments, the Reliability-Latency Optimized Polar Graph Generator (RLO-PGG) integrates user mobility patterns and delay spread profiles into a dynamic bit-channel assignment strategy, thereby reducing latency by 20–30% under high-speed scenarios. The thermodynamic Core Balancer for Polar Decoding (TCB-PD) introduces thermal-aware task scheduling that reduces energy consumption by 28–32% without compromising the integrity of decoding. Finally, the Hybrid Interleaved Polar Puncturing Optimizer (HIPPO) achieves coding rate flexibility with the help of a bit-difficulty predictor and hybrid interleaving, while enhancing throughput by up to 15% during constrained bandwidth regimes. Collectively, these techniques create a combined framework that significantly enhances the polar code decoding performance and presents a scalable solution for real-time implementation in next-generation wireless systems.