###### Abstract  
This study investigates the performance of different maximum power point tracking (MPPT) methods in a photovoltaic (PV) energy system, focusing on Artificial Neural Networks (ANNs), reinforcement learning (RL), and conventional MPPT approaches. The primary objective is to evaluate the efficiency of these methods in maximizing PV energy production under varying environmental conditions, while also analyzing their impact on battery state-of-charge (SOC) and load management. The system is modeled using MATLAB, incorporating real-world climate data from Ankara, including monthly solar irradiance, temperature, and sunlight hours. The ANN-based MPPT employs a multi-layer perceptron (MLP) to improve PV efficiency, while the RL-based MPPT utilizes Q-learning to optimize energy production. A conventional MPPT method serves as a baseline for comparison. Simulations are conducted on an hourly and monthly basis, considering a 7.5 kW PV system with a 20 kWh battery system. The results indicate that both ANN and RL methods outperform the conventional MPPT in terms of annual energy production, with RL achieving the highest efficiency gains. Additionally, the ANN and RL methods demonstrate improved battery SOC management, reducing energy losses. The study concludes that advanced MPPT techniques, particularly RL, offer significant potential for enhancing PV system performance, making them viable solutions for renewable energy optimization. of PV systems by dynamically adjusting their operating points under varying environmental or load conditions [9]. MPPT algorithms can be broadly classified into conventional methods, Artificial Neural Network (ANN)-based methods, and machine learning (ML)-based methods. The Perturb and Observe (P and O) method perturbs the operating point of the PV system and observes the resulting change in power. If the power increases, the perturbation continues in the same direction; otherwise, the direction is reversed [7]. While P and O is simple and effective, it may fail to track the MPP accurately under rapidly changing conditions [10]. The Incremental Conductance (IC) method calculates the derivative of the power with respect to voltage to determine the direction of the MPP. It offers better accuracy than P and O but requires more computational resources [11]. The Constant Voltage (CV) method assumes that the MPP occurs at a fixed voltage, typically around 70-80% of the open-circuit voltage. While this method is easy to implement, it is less accurate and may not adapt well to varying conditions [12]. Conventional MPPT methods, such as P and O and IC, have been widely used due to their simplicity, ease of implementation, and effectiveness under steady-state conditions. However, these methods often struggle to adapt to rapidly changing environmental conditions. They also have limitations when it comes to handling the nonlinear and time-varying characteristics of PV systems. This has led to the development of advanced MPPT methods based on ML and artificial intelligence (AI). Recent advancements in AI and ML have introduced novel approaches, such as ANN and reinforcement learning (RL), which offer improved adaptability and efficiency in MPPT applications [13]. Fuzzy Logic Control (FLC) uses fuzzy logic rules to determine the optimal operating point of the PV system. It is robust and adaptive but requires careful tuning of the membership functions and rules [14]. ANNs are computational models inspired by the structure and function of biological neural networks. They are capable of learning complex patterns and relationships from data, making them suitable for MPPT applications in PV systems [15]. ANN-based MPPT methods typically involve training a neural network to perform the MPPT based on input features such as solar irradiance, temperature, and load conditions. Several studies have demonstrated the effectiveness of ANN-based MPPT methods. For instance, ref. [16] proposed an ANN-based MPPT algorithm that achieved higher accuracy and faster convergence compared to traditional methods.