The article introduces a new system that uses machine learning to help optimize spatial join operations, which are used to combine data based on their locations. The system can handle different types of spatial datasets and algorithms, taking into account factors like data distribution and the logic of the algorithms. By training machine learning models on key features of the data, the system can estimate the cost of different join algorithms, predict the number of comparisons needed, and choose the best algorithm to use. Experiments show that this approach is more efficient than traditional methods when dealing with large amounts of data.