<p>Fog computing is a decentralized computing paradigm that provides distributed computing capabilities closer to the edge devices. The Internet of Things (IoT) is a wireless network used for collecting and transmitting data over the internet. Fog computing and IoT together enhance the quality of service by providing mobility support and low-latency communication in smart farming environments. However, data communication in smart farming is vulnerable to various security threats. Security in fog-enabled smart farming is essential to ensure the safety, privacy, and integrity of services. Many researchers have applied machine learning and deep learning techniques to address these security challenges. Existing methodologies and research gaps are analyzed in fog computing environments for secure communication in smart farming applications. The performance evaluation is carried out using different metrics, namely data confidentiality, data integrity, transmission time, and computational complexity. The major limitations of existing approaches are also discussed along with future research directions. In this survey, a literature table is presented to summarize existing works, including their objectives, results, and limitations in a concise manner. The results demonstrate that deep learning-based intrusion detection systems achieve the highest data confidentiality (96%) and data integrity (97%), with reduced transmission time (8&#xa0;ms) and computational complexity (12 MB). The findings emphasize the importance of hybrid approaches combining blockchain, machine learning/deep learning, and authentication protocols for secure and scalable smart farming deployments. This paper concludes by identifying research gaps and future directions toward resilient, real-time, and privacy-preserving agricultural systems.</p>