The reliability of power grid transformers is a cornerstone of socio-economic stability in the Cauca Department, Colombia, where diverse geographical conditions and intense ceraunic activity pose significant risks to critical infrastructure. Traditional predictive maintenance models, while accurate, often suffer from high computational overhead and latency, limiting their deployment in edge-computing environments. This paper introduces a Hyperdimensional Regression (HDR) framework as a computationally agile, energy-efficient alternative to conventional ensemble learners for power systems regression. Leveraging a comprehensive dataset of 15,873 transformers, we employ 2019 operational data for model training and 2020 data for rigorous temporal validation, ensuring practical applicability in predicting future infrastructure states. Experimental results demonstrate that while ensemble methods such as XGBoost yield high peak accuracy, the proposed HDR framework achieves robust predictive performance with $R^2$ scores reaching $65.5\%$ at a fraction of the computational cost. Notably, the HDR models maintain sub-second, deterministic inference latencies, proving to be approximately $17\times$ faster than high-performing baselines like XGBoost. This study establishes HDR as a superior high-fidelity, low-latency alternative for real-time fault detection in power distribution networks, providing utility providers with a scalable tool for optimizing maintenance schedules in both rural and urban grids.