Yes, you can integrate multiple ML or DL models trained on different datasets and diverse inputs. Think of it as orchestrating experts with different knowledge to solve a complex problem. Here are common approaches:

Ensemble Learning: Combine multiple models' predictions to create a more robust and accurate one. Think of it as a panel of experts voting on the best answer.

Stacking: Train a meta-model to learn how to best combine the predictions of individual models. Like having a manager who knows how to weigh each expert's opinion.

Pipelines: Chain models together sequentially, where each model's output becomes the input for the next. Like an assembly line, where each expert adds their expertise.

Multimodal Models: Design models that handle multiple input types, like text and images, fusing information from different sources. Like a multi-lingual expert who can integrate knowledge from different languages.