In today's environmentally conscious era, with challenges ranging from air quality and water pollution to radiation issues, the importance of thorough monitoring has become more pronounced than ever. Recent breakthroughs in technology, particularly the rapid evolution of the Internet of Things (IoT) and innovative sensor technologies, have ushered in an era of sophisticated and intelligent environmental monitoring (IEM). This manuscript offers a comprehensive review of groundbreaking contributions and research undertakings within the realm of IEM. The domains covered span the spectrum, from the meticulous tracking of air and water quality to the discerning evaluation of radiation pollution and agricultural systems. These investigations are classified according to their precise objectives, with each scrutinized in terms of the sensor technologies deployed, deep learning methodologies applied, and classification techniques employed. The culmination of this meticulous analysis yields recommendations and insights that underscore the profound influence of IEM research on the evolution of environmental monitoring practices. The authors critically assess how the synergy of deep learning, sensor innovations, and IoT connectivity is reshaping environmental monitoring into an astute and efficient system. This paper advocates for the formulation of robust deep learning methodologies, the refinement of denoising strategies, and the establishment of stringent standards for wireless sensor networks (WSNs). It is within this framework that the future of IEM is envisioned, promising to elevate precision and reliability in environmental monitoring endeavors.