In the era of smart energy systems, maximizing the efficiency of solar power conversion is more crucial than ever. This research presents a new solar power conversion system that utilizes advanced Deep Learning maximum power point tracking integrated with a novel Hybrid Cascaded H-Bridge Multilevel Inverter to provide optimal energy extraction and quality AC power. Through using of the Deep Neural Network, the system effectively follows operation at the maximum power point of the photovoltaic assembly to different stochastic characteristics like solar irradiation and temperature to improve power harvesting. Inverter is available in 5-level, 9-level, and 17-level configurations to maintain systems simplicity and yet have low Total Harmonic Distortion. Adopting Space Vector Pulse Width Modulation, the inverter produces power that is AC and the respective Total Harmonic Distortion values are 3.1 , 2.5 , and 1.6 for the 5-level, 9-level, and 17-level configurations, respectively. The proposed Deep Learning-based maximum power point tracking algorithm increases the system efficiency by approximately 15 higher than conventional techniques such as P&O. Claims on both replication and experiments show that the possible peak system efficiency is as high as 98.6 , while in the maximum power point tracking, averaging time converges in 35 ms. The presented integrated system is an efficient, modular concept for residential as commercial scale PV systems, maximizes the extracted power, particularly in rural locations, and ensures dependable power for medical equipment with premium quality AC with low total harmonic distortion. © 2025 Elsevier B.V., All rights reserved.