Bird identification has advanced thanks to the incorporation of technological tools, which offer better results and facilitate the process compared to traditional methods based on direct observation and field guides. The main objective is to analyze how mobile applications, IoT devices, and the use of machine learning can transform bird monitoring practices. Articles from several academic databases were reviewed using inclusion and exclusion criteria, allowing us to identify trends, advances, and challenges in these tools. Our results confirm that these tools not only improve accessibility and citizen participation but also contribute significantly to the accuracy, efficiency, and reach of habitat conservation and protection strategies. In turn, the use of machine learning and bioacoustics accelerates the identification process and the accuracy of results. However, challenges are also identified, such as the presence of ambient noise that can affect bird song recordings, the battery life of IoT devices, and interoperability issues between systems. It is necessary to foster interdisciplinary collaboration, where specialized knowledge from the sector contributes in an articulated manner, along with active community participation. Only in this way will it be possible to develop sustainable, precise, and adaptable solutions that support the preservation of bird habitats.