The authors advocated for hybrid and intelligent optimization techniques, which offer improved performance at the expense of higher computational complexity and cost, suggesting that the trade-off is justified in dynamic environments. In addition to conventional approaches, the scientific community has increasingly explored the application of soft computing and evolutionary algorithms for MPPT control in photovoltaic systems. As highlighted by [PERSON] et al. [13], advanced control techniques such as fuzzy logic control (FLC) [14] and adaptive neuro-fuzzy inference systems (ANFISs) [15; 16] have shown strong capability in managing the non-linear and time-varying behavior of PV systems. In parallel, a wide range of bio-inspired optimization algorithms have emerged as promising alternatives for global optimization. Among these, techniques such as genetic algorithms (GAs) [17], cuckoo search (CS) [18], ant colony optimization (ACO) [19], bee colony algorithm (BCA) [20], bat-inspired optimization (BAT) [21], and the memetic salp swarm algorithm [22] have demonstrated considerable effectiveness. These methods are particularly well-suited to address the challenges posed by partial shading conditions as they enable good exploration of the search space and enhance the system's ability to reliably detect the GMP. According to [PERSON] et al. [23], both soft computing and evolutionary techniques exhibit enhanced adaptability and are capable of delivering efficient and reliable tracking performance even under highly dynamic and non-linear operating conditions. Additionally, recent works have explored data-driven and neural network-based methods that, while applied in different domains, offer valuable insights for adaptive control strategies in PV systems. For example, [PERSON] et al. [24] proposed a transfer learning framework to enhance adaptability in dynamic environments, while [PERSON] and [PERSON] [25] introduced a multilayer neural network approach for intelligent frequency domain filtering. These contributions reinforce the relevance of learning-based techniques in handling non-linear time-varying conditions. Lastly, [PERSON] et al. [26] compared traditional and metaheuristic MPPT techniques. Their findings reinforce that, although conventional algorithms such as P&O are easier to implement, they struggle in PSC scenarios. Although previous studies have provided valuable insights, the majority of works in the literature still present notable limitations that prevent a comprehensive evaluation of MPPT strategies. Many focus on comparing only two or three algorithms, often within limited and idealized scenarios. Furthermore, performance assessments are frequently restricted to a few basic metrics, such as efficiency or steady-state accuracy, overlooking crucial dynamic aspects like convergence time or tracking stability under complex PSC. In particular, convergence time is an essential indicator of system responsiveness that remains underexplored and rarely quantified rigorously. Additionally, detailed scenario characterization, especially for PSC cases, is often lacking or simplified. To address these gaps, this work presents a comprehensive comparative analysis of MPPT algorithms, including conventional methods (P&O and InC), control logic (FLC), hybrid (GA-based), and intelligent approaches (ANNs and DQNs). All the methods are evaluated under both uniform and partial shading conditions using a modular MATLAB/Simulink framework. Multiple performance metrics--MAE, IAE, MSE, ISE, efficiency, and convergence time--are considered. Convergence time is calculated using formal signal analysis with graphical validation. Test cases are clearly defined with specific irradiance and temperature settings, ensuring transparency. This study offers deeper insight into the strengths and limitations of MPPT strategies, especially AI-based ones, to support their practical application in PV systems. The article is organized as follows. Section 2 describes the photovoltaic system modeling and simulation setup.