With the growing need for clean energy, renewable sources such as solar photovoltaic (PV) systems are becoming more vital. To optimise photovoltaic systems in varying weather conditions, it is essential to use Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques are often utilized such as Perturb and Observe and Incremental Conductance. Nonetheless, these approaches exhibit issues such as sluggish tracking velocities and persistent oscillations due to predetermined step sizes. Soft computing techniques, such as artificial intelligence and evolutionary algorithms, exhibit more flexibility; nonetheless, their efficacy diminishes in the absence of sufficient unpredictability. This may lead to their rapid convergence at the optimal power point. This work proposes the implementation of a Flower Pollination Algorithm (FPA) to develop a Maximum Power Point Tracking (MPPT) controller for solar energy systems. FPA is based on the mutual pollination of flowering plants in nature. It integrates global and local search into a single optimisation process, enabling the identification of the maximum power point despite fluctuations in irradiance levels. The system design entails transmitting the output voltage and current from the solar panel to a DC-DC boost converter. The FPA-based MPPT controller then determines the optimal duty cycle to maximise power extraction from the system. We used MATLAB/Simulink to develop and evaluate the proposed method. It exhibited superior tracking capabilities compared to conventional MPPT techniques due to its enhanced accuracy and speed.