The deployment of intelligent algorithms in ecological research is increasingly prevalent, addressing numerous significant issues. The application of intelligent algorithms is emerging as a critical area of focus within ecological research. Firstly, based on the CiteSpace.6.3R1 econometric analysis visualization software, 42439 papers related to the application of intelligent algorithms in ecology from 2013 to 2023 and found in the CNKI (China National Knowledge Infrastructure) and WOS (Web of Science) core collections were taken as study objects to assess global and domestic trends and hotspots. Subsequently, this study discusses the research processes and application traits of intelligent algorithms in ecological optimization, prediction, and evaluation. The results demonstrate that: (1) In the field of ecological optimization, the Particle Swarm Optimization Algorithm (PSO) is effective for addressing problems involving continuous variable changes, while the Genetic Algorithm (GA) is more appropriate for scenarios characterized by discrete variables. Multi-agent Systems (MAS) exhibit advantages in solving sequential decision-making challenges. (2) In the domain of ecological prediction, deep learning algorithms are effective at handling high-dimensional and complex datasets, whereas Support Vector Machines (SVM) demonstrate efficiency and reliability when applied to smaller datasets. (3) Regarding evaluation models in ecological research, although individual models, such as the Decision Tree (DT), are prone to overfitting, they are well-suited for small datasets. In contrast, ensemble algorithms such as Random Forest (RF) and Gradient Boosting Decision Trees (GBDT) mitigate overfitting through their multi-tree structures, thereby enhancing generalization capacity and prediction accuracy. Furthermore, the advantages and limitations of the application of intelligent algorithms have been analyzed. Despite the potential of intelligent algorithms to optimize complex ecological systems and forecast future scenarios accurately, their application is constrained by several limitations. Many algorithms rely on manually specified input parameters and suffer from low interpretability, particularly in the case of deep learning algorithms, where understanding the rationale behind model decisions presents a significant challenge. Meanwhile, the inherent high-dimensional complexity and uncertainty of ecological systems place greater demands on intelligent algorithms. Thus, improving the applicability and stability of these algorithms is a key challenge that needs to be addressed. The advancement of hybrid intelligent algorithms holds promising potential for overcoming these limitations, improving both computational efficiency and predictive accuracy. In conclusion, this study reviews the significance of intelligent algorithms in ecological research and proposes potential directions for future advancements. It underscores the importance of interdisciplinary collaboration, advocating for closer partnerships between ecologists and computer scientists to jointly develop algorithms that more effectively address the specific demands of ecological research.