To address the escalating challenges in agricultural production, there is a critical need to enhance our understanding of complex farm ecosystems. Leveraging modern digital technologies to monitor the physical environment continuously generates vast amounts of data at an unprecedented pace. Big data analytics’ analysis of this sizable dataset has the potential to benefit businesses and farmers by revealing insightful information that will increase productivity. Despite big data’s transformative impact in various industries, its widespread application in agriculture remains limited. This paper conducts a comprehensive review of current studies and research in agriculture that incorporate big data analysis, aiming to address various pertinent challenges. Examining 34 diverse studies, our review delves into the identified problems, proposed solutions, and the tools, algorithms, and data employed. Notably, the review scrutinizes the nature and dimensions of big data, the scale of its application, and the overall impact. Crucially, we emphasize the substantial opportunities big data analysis presents in agriculture for cultivating smarter farming practices. Incorporating machine learning and artificial intelligence algorithms into this analysis further amplifies its potential. The availability of technology, software, and growing methodologies for big data analysis, together with the rising openness of data sources, is set to boost research, public sector efforts, and agricultural commercial enterprises. However, it is imperative to acknowledge that this practice is still in its early developmental stage, and several barriers must be surmounted to fully realize its transformative potential.