<p>The growing concerns over environmental sustainability, coupled with the increasing demand for clean and efficient transportation, have prompted innovation in the field of Electric Vehicles (EVs) powered by Renewable Energy Sources (RESs). This research introduces a pioneering approach that harnesses Photovoltaic (PV) energy to propel EVs, utilizing an embedded system equipped with High Gain Improved Zeta (HGIZ) converter and Internet of Things (IoT). For optimizing the utilization of PV energy, a HGIZ converter is implemented in conjunction with a Modified Perturb and Observe (P&amp;O) Maximum Power Point Tracking (MPPT) algorithm, thereby ensuring that the PV system operates at its maximum efficiency. The Direct Current (DC) output from PV system serves a dual role: it powers the EV motor for eco-friendly propulsion and simultaneously charges the battery, making the vehicle self-sustaining with clean energy. Real-time monitoring of critical system parameters, including PV voltage, current, DC link voltage, battery State of Charge (SOC), EV motor data, and battery voltage, is achieved through a sophisticated sensor-equipped system. The data gathered from these sensors is processed by a high-performance Field Programmable Gate Array (FPGA) controller equipped with built-in Wi-Fi capabilities. The research incorporates a hybrid neural network, blending Deep Convolutional Neural Network (DCNN) and Bidirectional Long Short-Term Memory (BiLSTM) mechanisms to safeguard data transmission to cloud. Additionally, the research utilizes Improved Firefly Algorithm (IFA) for determining the data transfer path, optimizing the system's efficiency. The experimental validation is performed using Matlab Simulink, demonstrating enhanced converter efficiency of 97.8%. For user accessibility and monitoring, the embedded PV-powered EV system's status is conveniently accessed through an Adafruit Internet of Things (IoT) web page hosted in the cloud.</p>