The paper provides a comprehensive review of electronic nose (e-nose) technology, focusing on advancements and emerging applications over the last five years. E-noses, designed to replicate human olfaction, consist of a gas sensing system and an information processing unit. Since their inception in the 1980s, e-noses have evolved from bulky, costly devices to streamlined, energy-efficient models. The review highlights the transition from traditional reviews by synthesizing cutting-edge research, emphasizing new sensor technologies and computational methods. Key areas of application include food analysis, environmental monitoring, and medical diagnostics. In food analysis, e-noses are used for quality assurance, origin tracking, process optimization, and waste reduction. They can detect and analyze volatile organic compounds (VOCs) in various food products, ensuring safety and freshness. In environmental monitoring, e-noses are employed for air and water quality assessment, process control, worker health protection, and odor control system evaluation. They can identify pollution sources, enhance safety in industrial settings, and support sustainable waste management practices. In medical diagnostics, e-noses detect specific metabolic pathways associated with diseases, enabling early detection and intervention strategies. The paper also discusses the principles of odor sensors, gas sensing mechanisms, and the role of algorithms in e-nose systems. It explores various pattern recognition techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares regression (PLSR), support vector machines (SVMs), and artificial neural networks (ANNs). These techniques are crucial for data processing and classification in e-nose systems. Overall, the review underscores the adaptability and potential of e-noses in various industries, addressing gaps in current literature and suggesting avenues for future research.The paper provides a comprehensive review of electronic nose (e-nose) technology, focusing on advancements and emerging applications over the last five years. E-noses, designed to replicate human olfaction, consist of a gas sensing system and an information processing unit. Since their inception in the 1980s, e-noses have evolved from bulky, costly devices to streamlined, energy-efficient models. The review highlights the transition from traditional reviews by synthesizing cutting-edge research, emphasizing new sensor technologies and computational methods. Key areas of application include food analysis, environmental monitoring, and medical diagnostics. In food analysis, e-noses are used for quality assurance, origin tracking, process optimization, and waste reduction. They can detect and analyze volatile organic compounds (VOCs) in various food products, ensuring safety and freshness. In environmental monitoring, e-noses are employed for air and water quality assessment, process control, worker health protection, and odor control system evaluation. They can identify pollution sources, enhance safety in industrial settings, and support sustainable waste management practices. In medical diagnostics, e-noses detect specific metabolic pathways associated with diseases, enabling early detection and intervention strategies. The paper also discusses the principles of odor sensors, gas sensing mechanisms, and the role of algorithms in e-nose systems. It explores various pattern recognition techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares regression (PLSR), support vector machines (SVMs), and artificial neural networks (ANNs). These techniques are crucial for data processing and classification in e-nose systems. Overall, the review underscores the adaptability and potential of e-noses in various industries, addressing gaps in current literature and suggesting avenues for future research.