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Abstract: The goal of this project is to create a smart home garden monitoring system utilising Python, Arduino, and an ESP32 microcontroller to track environmental variables for the best plant growth. The Arduino and ESP32 microcontrollers are connected to sensors for temperature, humidity, soil moisture, light, and carbon dioxide levels. Machine learning algorithms are used to assess the acquired data and calculate the ideal circumstances for plant growth. The device uses battery backup technology to assure continuous operation and is fueled by renewable energy sources like solar and wind energy. Due to the system's internet connection, it is possible to remotely watch the garden from any location in the world. A mobile application or online interface that offers real-time information on the health of the plants and environmental conditions can be used by the user to access the system. The system has a mobile communication capability that notifies the user when the plants are in good health. The system notifies the user via a mobile application when an alarm threshold is surpassed and allows the user to define the alert thresholds for various criteria. With the use of this tool, the user can prevent plant harm by acting quickly. The project's main goal is to enable people to grow their own veggies using a smart home garden monitoring system powered by Python, Arduino, and ESP32 microcontroller and Internet of Things (IoT) technologies, while also lowering the carbon impact of food production.