By integrating innovative technologies to enhance the efficiency and sustainability of production, this study specifies the establishment of a cutting-edge growing system for Chlorella sp. microalgae. Improvement of a system for the real-time, noninvasive observation and management of algae growth employing a closed tubular photobioreactor (PBR) engineered with computational fluid dynamics (CFD), combined with the Internet of things (IoT), artificial intelligence (AI), and image processing technologies was the major goal of this research. The fitting of seven types of sensors to identify key characteristics such as temperature, pH, light intensity, electrical conductivity (EC), flow rate, oxygen content, and light exposure duration was included in the research method. To manage the gaining of sensor data and system operations, an ESP8266 microcontroller was used as the main control unit, while 33 × 33 pixel images were taken with an ESP32 camera at 30 min intervals to assess growth by evaluating color intensity, enabling real-time evaluation of algal density without sampling or disturbing growth. Forecasting and enhancing farming situations was the goal of producing these machine learning (ML) models. Uniformly dispersed between 12 and 24 h light cycles, the data set comprised 602 samples. Considerable improvements were observed in the results for biomass productivity, with constant 24 h lighting yielding a 7.19% increase, counter to a 2.09% increase seen in the 12 h cycle. Temperature and light intensity are the most significant parameters for growth, as revealed by analysis of Feature Importance. The eXtreme Gradient Boosting (XGBoost) model showed remarkable effectiveness in terms of projecting growth, attaining an R 2 value of 0.9997 for the training data set. With important benefits for the development of renewable energy, food supply, and environmental modification in the future, this research highlights the competence of intelligent technology to strengthen microalgae production.
