Wireless Sensor Networks (WSNs) have gained popularity in fields like environmental monitoring, smart agriculture, and industrial automation. However, the main challenge is optimizing sensor placement to ensure coverage, energy efficiency, and fault tolerance. Traditional approaches, such as clustering, greedy algorithms, and heuristic methods like Genetic Algorithm (GA) or Particle Swarm Optimization (PSO), often struggle with trade-offs between coverage and energy consumption, resulting in suboptimal network lifetimes. The paper proposes an Energy-Efficient Sensor Placement Framework using Simulated Annealing (SA). SA helps balance coverage, energy efficiency, and fault tolerance by strategically placing sensors to maximize coverage while minimizing energy usage. The proposed approach includes sensor power decay models, failure recovery strategies, and cluster-based communication to enhance network robustness. Simulations show a 20% improvement in energy efficiency, a 15% increase in network lifetime, and better fault recovery than GA and PSO-based methods, making it more efficient for large-scale deployments.