Systems and methods for utilizing machine learning to generate network configurations

A device, comprising:

• one or more processors configured to:
• receive network data associated with networks, network devices, ports, bandwidth usage, and address assignments of a network architecture;
• predict network types for the network architecture based on analyzing locations, network capacities, utilizations, and total customers identified in the network data;
• process the network data, with one or more machine learning models, to determine device types, detected ports, bandwidth, and available addresses for the network architecture;
• generate network device configurations and network configuration changes based on the network types, the device types, the detected ports, the bandwidth, and the available addresses;
• implement the network device configurations and the network configuration changes in the network architecture;
• receive feedback associated with implementing the network device configurations and the network configuration changes in the network architecture;
• and
• update one or more of the network device configurations and one or more of the network configuration changes based on the feedback and to generate one or more updated network device configurations and one or more updated network configuration changes.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive network data associated with networks, network devices, ports, bandwidth usage, and address assignments of a network architecture;
• predict network types for the network architecture based on analyzing locations, network capacities, utilizations, and total customers identified in the network data;
• process the network data, with one or more machine learning models, to determine device types, detected ports, bandwidth, and available addresses for the network architecture,
• wherein the one or more machine learning models include one or more of a decision tree model, a random forest model, a k-nearest neighbors model, a logistic regression model, a time series analysis model, an autoregressive integrated moving average model, a long short-term memory model, a graph theory model, a constraint satisfaction problem solver model, a heuristic model, a rule-based system, or a support vector machine model;
• generate network device configurations and network configuration changes based on the network types, the device types, the detected ports, the bandwidth, and the available addresses;
• and
• implement the network device configurations and the network configuration changes in the network architecture.