<p>Recent advancements in the Internet of Things (IoT) have heightened the demand for energy efficient, high-performance Very Large-Scale Integration (VLSI) circuits. While machine learning has been widely employed for performance analysis and optimization, the integration of quantum computing concepts is reshaping this domain. This paper presents a comprehensive framework that leverages both classical deep learning and quantum-enhanced algorithms to analyze and optimize low-power VLSI circuits in IoT devices. Advanced Quantum AI techniques—specifically, the Proposed Quantum Support Vector Machine (QSVM)—are applied alongside conventional models (QANN, QCNN, QLSTM, and QAdaptive CNN-LSTM) to enable high-dimensional feature transformations and exponential parallelism in analysis. Experimental results demonstrate that the QAdaptive CNN-LSTM achieves 94.5% accuracy, 92.0% precision, 93.0% recall, 92.5% F1-score, and an MSE of 0.05, significantly outperforming QANN (82.5% accuracy, 81.2% precision, 81.0% recall, 81.1% F1, MSE 0.15), QCNN (86.5% accuracy, 84.0% precision, 81.5% recall, 82.7% F1, MSE 0.11), and QLSTM (84.0% accuracy, 83.5% precision, 85.5% recall, 84.5% F1, MSE 0.20). Moreover, the Proposed QSVM model attains the highest accuracy of 96.0%, with 94.5% precision, 95.0% recall, 94.7% F1-score, and the lowest MSE of 0.035, underscoring the strong potential of quantum-enhanced learning in VLSI analysis. These quantifiable improvements translate into superior prediction accuracy, faster convergence, and better energy-timing optimization under resource-constrained IoT scenarios. The hybrid quantum–classical analytical approach not only enhances predictive maintenance and optimization of VLSI circuits but also accelerates the development of sustainable, scalable, and energy-efficient IoT ecosystems.</p>