Nonlinear characteristics of solar photovoltaic (PV) and nonuniform surrounding conditions, including partial shading conditions (PSCs), are the major factors responsible for lower conversion efficiency in solar panels. One major condition is the cause of the multiple peaks and oscillation around the peak point leading to power losses. Therefore, this study proposes a novel hybrid framework based on an artificial neural network (ANN) and fractional order PID (FOPID) controller, where new algorithms are employed to train the ANN model and to tune the FOPID controller. The primary aim is to maintain the computed power close to its true peak power while mitigating persistent oscillations in the face of continuously varying surrounding conditions. Firstly, a modified shuffled frog leap algorithm (MSFLA) was employed to train the feed-forward ANN model using real-world solar PV data with the aim of generating a reference solar PV peak voltage. Subsequently, the parameters of the FOPID controller were tuned through the application of the Sanitized Teacher–Learning-Based Optimization (s-TLBO) algorithm, with a specific focus on achieving maximum power point tracking (MPPT). The robustness of the proposed hybrid framework was assessed using two different types (monocrystalline and polycrystalline) of solar panels exposed to varying levels of irradiance. Additionally, the framework’s performance was rigorously tested under cloudy conditions and in the presence of various partial shading scenarios. Furthermore, the adaptability of the proposed framework to different solar panel array configurations was evaluated. This work’s findings reveal that the proposed hybrid framework consistently achieves maximum power point with minimal oscillation, surpassing the performance of recently published works across various critical performance metrics, including the $MP{P}_{efficiency}$, relative error (RE), mean squared error (MSE), and tracking speed.