Globally agriculture has remained a key factor in food security, employment, and several other favorable economic indices. However, factors like rising world population, trade globalization, and climate variabilities have created the need for modernization and optimization to boost production and livelihood. Machine learning allows machines to read from a pool of available data and provide data-centric results. This has opened up a new and promising perspective. The paper examines recent proven works in machine learning technology application in agriculture to establish the modest contribution of machine learning and emerging deep learning technologies in this field to highlight the need for its adoption in the Nigerian agricultural ecosystem. Therefore, a systematic review was carried out using a categorization model of key agricultural subsectors/activities. Findings have shown a widespread of its application with significant positive impact in almost every aspect of agriculture with new works showing higher result efficiency in deep learning technologies application. Insightful recommendation from these technologies has proven capable of boosting agriculture on various fronts. Thus, the adoption of ML/DL technologies in Nigeria’s Agriculture will go a long way in helping the country attain food sufficiency.