[Python] How can I link tasks using machine learning / ai based on historical task sequences?...

I'm working on an AI model to predict dependency links between tasks for industrial plannifications, based on historical project data. I have two tables:

Tasks Table: TaskID, TaskName, EquipmentType (I'm considering adding StartDate and EndDate)

Dependencies Table: PredecessorTaskID, SuccessorTaskID, LinkType

Goal: Given a new list of tasks (typically filtered by EquipmentType), I want the model to suggest likely dependencies between them (and eventually, the LinkType) — learned from historical patterns in the existing data.

What I’ve tried:

Decision Trees

Basic Neural Networks

Problems encountered:

Random or irrelevant links

Models predicting dependencies between all tasks

Lack of logical flow learned from historical data

I'm pretty sure i am not pre-processing the data correctly as i'm not sur how to treat the tasks name for it to recognize the "pattern"

My Question: Would it make sense to frame this as a graph problem and use Graph Neural Networks (GNNs)? Or is there a better ML or statistical approach for modeling and predicting dependencies between tasks in this kind of scenario?

I'm open to advice on model architecture or data pre-processing strategies that might improve performance.

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