This review article explores the integration of IoT and AI technologies in vertical farming (VF) to enhance efficiency, sustainability, and productivity. Vertical farming, a soil-free method of growing crops indoors, requires minimal land and water compared to traditional farming. With the global population expected to rise significantly, VF offers a promising solution to food shortages and resource constraints. However, VF faces challenges in monitoring multiple indicators, providing nutrition advice, and diagnosing plant issues. These challenges can be addressed through AI-based technologies such as machine learning (ML), deep learning (DL), IoT, image processing, and computer vision. The article discusses the application of ML and IoT in VF systems, focusing on areas like disease detection, crop yield prediction, nutrition, and irrigation control. Computer vision techniques are used to classify crop images for disease detection and yield prediction. ML and IoT-based VF systems can improve product quality and production over the long term. The article also evaluates the knowledge-based VF system, highlighting the potential, advantages, and limitations of ML and IoT in VF. The review covers various types of VF systems, including hydroponics, aeroponics, and aquaponics. Hydroponics, which grows plants in nutrient-rich solutions, is discussed in detail, including its components, types of systems, and advantages. Aeroponics, which suspends plant roots in the air and delivers nutrients through mist, and aquaponics, which combines fish farming with plant growth, are also explored. Machine learning is a subset of computer science that enables systems to learn from data without explicit programming. ML algorithms are categorized into supervised, unsupervised, and reinforcement learning. Performance metrics are used to evaluate ML models, including regression and classification metrics. IoT, a network of interconnected devices, plays a crucial role in VF by enabling real-time monitoring and control of environmental factors such as temperature, humidity, and light. IoT systems include sensors, gateways, and cloud-based processing to collect and analyze data for optimal plant growth. The review highlights the potential of IoT and AI in transforming VF into a sustainable and efficient agricultural practice. However, challenges such as energy consumption and land use must be addressed to ensure the long-term viability of VF. The integration of IoT and AI in VF offers a promising solution to the challenges of food production in the face of a growing population and environmental constraints.This review article explores the integration of IoT and AI technologies in vertical farming (VF) to enhance efficiency, sustainability, and productivity. Vertical farming, a soil-free method of growing crops indoors, requires minimal land and water compared to traditional farming. With the global population expected to rise significantly, VF offers a promising solution to food shortages and resource constraints. However, VF faces challenges in monitoring multiple indicators, providing nutrition advice, and diagnosing plant issues. These challenges can be addressed through AI-based technologies such as machine learning (ML), deep learning (DL), IoT, image processing, and computer vision. The article discusses the application of ML and IoT in VF systems, focusing on areas like disease detection, crop yield prediction, nutrition, and irrigation control. Computer vision techniques are used to classify crop images for disease detection and yield prediction. ML and IoT-based VF systems can improve product quality and production over the long term. The article also evaluates the knowledge-based VF system, highlighting the potential, advantages, and limitations of ML and IoT in VF. The review covers various types of VF systems, including hydroponics, aeroponics, and aquaponics. Hydroponics, which grows plants in nutrient-rich solutions, is discussed in detail, including its components, types of systems, and advantages. Aeroponics, which suspends plant roots in the air and delivers nutrients through mist, and aquaponics, which combines fish farming with plant growth, are also explored. Machine learning is a subset of computer science that enables systems to learn from data without explicit programming. ML algorithms are categorized into supervised, unsupervised, and reinforcement learning. Performance metrics are used to evaluate ML models, including regression and classification metrics. IoT, a network of interconnected devices, plays a crucial role in VF by enabling real-time monitoring and control of environmental factors such as temperature, humidity, and light. IoT systems include sensors, gateways, and cloud-based processing to collect and analyze data for optimal plant growth. The review highlights the potential of IoT and AI in transforming VF into a sustainable and efficient agricultural practice. However, challenges such as energy consumption and land use must be addressed to ensure the long-term viability of VF. The integration of IoT and AI in VF offers a promising solution to the challenges of food production in the face of a growing population and environmental constraints.