Smart agriculture is an agricultural practice that uses sustainable methods to meet the growing food needs of the population. It also aims to develop, improve, and sustain the agricultural production system. On the other hand, the integration of modern information and communications technologies used in agricultural machinery and equipment, remote sensors, global positioning systems, and modern agricultural methods, which would increase productivity and quality without depleting natural resources, such as smart irrigation and precision agriculture, led to the development of smart agriculture. Moreover, agricultural practices also need to change because the climate is changing, so it has become necessary to implement more sustainable agricultural practices, to mitigate the pace of severe climate changes, whose goal is to improve the quality of the final agricultural product, increase productivity, and enhance the efficiency of resource use and the effectiveness of agricultural production systems, from point of view the cost. Smart agriculture currently depends on data generated from the Internet of Things devices in real time, geospatial data, and previous and historical information. However, employing big data analysis methods requires significantly diverse skills and deeper knowledge compared to what many possess, such as farmers, agricultural engineers, and graduates of agricultural colleges, which constitutes an obstacle to using this data effectively in making decisions in the agricultural field, etc. This study aimed to summarize and provide insight into smart agriculture tools and modern methods used to analyze data for several agricultural applications, with the aim of applying both smart agriculture methods and modern data analysis depending on the desired goals.