This research paper aims to examine the use of proposed AI techniques and their effectiveness when applied to developing countries. Developed countries have applied Sentiment Analysis to great effect in the past with significant results; this was possible due to foundational infrastructure and an accustomed urban population. For developing countries ushering into urban centers and lacking infrastructure, smart city planning and maintenance must adapt to the unique behavioral, socioeconomic, and bureaucratic challenges. Sentiment Analysis of publicly obtained data coupled with cooperation from local authorities could prove instrumental in significantly improving the quality of life in smart cities. The analysis could provide immediate relief from problems with simple solutions that usually evade easy and prompt discovery by the authorities. As countries continue to develop, the analysis of existing smart cities could provide guidelines to consult when planning new smart cities. Employing AI-based planning algorithms coupled with Big Data Analytics shows considerable promise here; the concentration of highly skilled employees and their migration patterns as work-from-home policies allow for completely remote employment opportunities could be a big factor in selecting areas for rigorous urbanization. Smart city planning would benefit a lot from AI-assisted disaster prevention and management considerations: the placement of roads, the shape of apartment blocks, the size of their foundation, occupational capacities, etc. could prevent loss of life and property in events of disaster. This paper aims to address these challenges and examine their pre-existing solutions.