This paper explores the harnessed potential of machine intelligence algorithms in the domain of plant disease detection. Agriculture remains the cornerstone of economic development, especially in countries like India. Thus, the need to secure robust crop yields is increasingly paramount. This study provides a comprehensive analysis of computational intelligence algorithms, shedding light on how they are used to significantly enhance the efficiency of plant disease detection. Furthermore, a detailed examination of a wide range of associated methodologies and performance metrics is undertaken. Beyond these dimensions, the paper investigates and endures challenges that are encountered in this field, offering innovative solutions. The analysis highlights the pivotal role played by machine learning (ML) and deep learning (DL) algorithms in advancing the field of plant disease detection. Additionally, this paper delves deeper into the nuanced advantages and constraints inherent in these approaches, subjecting their performance to scrutiny under various critical parameters.