Renewable sources, such as photovoltaic systems and wind turbines, have been widely employed in the power industry to implement greener energy solutions for electricity generation. However, concerns regarding their efficiency and costs have been prominent. The concept of maximum power point tracking has emerged to address these concerns. This paper focuses on integrating an adaptive incremental conductance (INC)-based MPPT algorithm to control the output of a DC-DC converter for tracking maximum power via a microcontroller. The algorithm provides an energy-monitoring system that utilizes an online IoT control system platform, facilitating the recording and storage of data accessible through various devices. By incorporating the output parameters of the solar panel to determine the duty cycle provided to the DC-DC converter, the tracking time has been significantly reduced from 30 to 20 seconds using the adaptive INC-based MPPT algorithm. This improvement in tracking time has led to enhanced power generation, reliability, and system stability. The test was conducted in the Philippines during peak sun hours, specifically at 10 a.m. to 2 p.m. The results demonstrated that the adaptive INC-based MPPT method tracked 95% of the maximum power at a speed of 25 seconds faster than the conventional incremental conductance method. The control system successfully received and recorded all the measured data, which was effectively analyzed and stored on the online monitoring channel.