Growing population has led to skyrocketing food demand, which has made precision agriculture and resource management one of the hot topics, especially with the improvements in information and communication technologies. Geographic information system (GIS) is successfully used in planning-related research. However, the limited intelligence to handle uncertainties and subjectivity in real-world agricultural and resource-related problems is a major compounded issue in GIS-based planning research. To successfully overcome all these issues, experts from various fields including agriculture, geography, computer science, and remote sensingRemote sensing have developed several GIS-based fuzzy optimization models and tested them in the management of natural resources. This chapter provides a comprehensive analysis of all the existing research on GIS-based fuzzy optimization techniques for precision agriculture and natural resource management. GIS based fuzzy (GISF) now plays a vital role in agriculture, and many of its parameters are largely fuzzy in nature. Here, the uncertainty associated with the fuzzy land unit is sought to be optimized through the application of various optimization techniques within a GIS. GIS facilitates the spatial expression of all structural parameters of physical and economic systems in agriculture or any other sector. Moreover, research efforts in the area of GIS-based fuzzy optimization are relatively less than those done for the deterministic type of analyses. These hybrid analytical techniques could be fruitfully utilized in precision agriculture, water resource management, pollution control, ecology conservation, forest and fisheries management, animal husbandry, etc. This chapter attempts to address and re-explore the robustness of the GIS-based fuzzy optimization techniques and aims to use them in various management strategies in agriculture, leading to enhanced resource and management model precision. The underlying reason for the fuzzification of these determinants, as usual, is the inherent difficulty associated with their measurements, cost matrixing, and general system complexity. Though very few existing methods based on regression techniques can address these basic issues in part, GIS-based representation of these structural parameters coupled with various special queries like overlay, buffering, nearest approach, etc., has not been examined in any of these studies. A careful review of related earlier research reveals that conventional optimization techniques are designed to work with crisp logic or probabilities, leaving the eminent GIS toolbox closed to “uncertain” membership-based fuzzy concepts. On the other hand, many management decisions with respect to these applications have to be taken, given various available possibilities, based on the expert’s often spatial judgment. Fuzzy, purely built on mathematical and logical grounds, plays a very useful role in decision support systems, especially in regions characterized by imprecision related to the parameters that affect the system’s response, i.e., in the areas of geography.