Abstract <p>Biochar plays a crucial role in regulating soil phosphorus (P) availability, yet its effectiveness is influenced by multiple factors, including biochar features and soil properties. Improper biochar application may reduce P availability towards plants or unintendedly increase the environmental risk of P leaching. The efficiency of biochar in regulating soil P availability can be predicted and quantified by analyzing the interactions between its physicochemical properties and soil conditions. This study employed machine learning models—Random Forest, Support Vector Regression, and Artificial Neural Networks—to predict biochar efficiency in soil P availability regulation (activation or passivation) using a dataset of 534 samples with 19 input features. Model optimization and evaluation revealed that the Random Forest model achieved the highest prediction accuracy (<i>R</i>² = 0.9107), outperforming the other two models. Mechanistic insights from feature importance analysis indicated that biochar pyrolysis temperature played a dominant role in influencing soil P availability. Moderate pyrolysis temperatures facilitated the formation of biochar with balanced porosity and surface reactivity, while biochar produced at higher temperatures favored for passivating soil availability. Furthermore, the biochar application rate, soil pH, and total soil P content are key factors influencing changes in soil available P following biochar amendment. Through a data-driven framework, this study demonstrated that pristine biochar could achieve or exceed the performance of modified biochar in P regulation, offering superior economic and environmental benefits. The findings integrated environmental science, soil chemistry, and data analytics, providing valuable guidance for precision agriculture and fostering sustainable agricultural practices by enhancing fertilizer efficiency and reducing environmental costs globally.</p> Graphical Abstract <p></p>