Neural networks (NN) and machine learning (ML) techniques are gradually replacing true critical thinking. These methods surpass logical and quantitative approaches because they can handle massive boundaries in massive amounts of data and dynamic behavior over time. This article lays the groundwork for adaptive traffic control by proposing ML and DL calculations for crossing point traffic flow prediction. Adaptable traffic control can be accomplished by adjusting traffic light timing based on expected flow or by controlling traffic lights partially. That is why traffic flow prediction is the only focus here. The suggested ML and DL models are created, approved, and tested using two publicly available datasets. The first one gives a total of all cars analyzed by various sensors over a 56-day period, including clocks placed at six different crossroads. The ML and DL models used in this study were developed using four out of the six crossings. Subsequently, Multi-facet Perceptron Brain Organizations (MLP-NN) dealt with Irregular Backwoods, Direct Relapse, and Stochastic Inclination; MLP-NN required less preparation time but produced better results (R-Squared and EV score of 1.93) than Intermittent Brain Organizations (RNNs), which produced excellent measurement results but required more time overall. All ML and DL computations have excellent execution results, which makes them a good fit for intelligent traffic light management. © 2024 Elsevier B.V., All rights reserved.