The evolution of 5G networks has introduced the concept of network slicing to cater to diverse applications like enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). This paper proposes an advanced energy-aware allocation model, incorporating SLA checks and dynamic user reassignment to optimize energy consumption and enhance QoS. Simulations reveal a 30% energy efficiency improvement and up to 10% better SLA compliance compared to traditional static resource allocation methods, proving the effectiveness of the proposed approach. The results are analyzed against critical QoS requirements like throughput, latency, jitter, and packet loss. In developing such a solution, the significant key contributions from the research conducted lie in resource-allocation strategy construction that will utilize base station levels with low utilization and a dynamic reassignment process that adapts to varying circumstances by allowing an efficient use of resources among UE slices to comply with their individual SLA for different requirements over time. The results of these experiments show that the proposed framework can properly handle the strict requirements for 5G services in terms of scalability and adaptability. Consequently, the study's implications for real-world 5G networks deploying such integrated allocation strategies are discussed in the concluding section, and avenues for future research about prediction-based mobility models and machine learning-based resource optimization are mentioned.