Accurate real-time prediction of formation pressure and kick detection is crucial for drilling operations, as it significantly enhances decision-making and cost-effectiveness. Data-driven models are increasingly popular in automating drilling operations through precise prediction of formation pressure and effective detection of kicks. However, there is a notable absence of publicly available datasets in current literature, which hinders technological advancements in this domain. This repository introduces two comprehensive datasets, now publicly released under appropriate licensing, to support researchers in developing intelligent algorithms that enhance oil/gas well drilling research. These datasets, which include data samples for formation pressure prediction and kick detection, feature 28 drilling variables and contain over 2000 data samples each. Additionally, we provide simulation coding and detailed data analysis methodologies. Principal component regression is employed to predict formation pressure, while principal component analysis is utilized to identify kicks, ensuring the dataset’s technical validation. This version contains the experimental codes for data collection and analysis. File description: Data_Collection_Test_OTR.zip -- This file contains the experimental codes for collecting data from the On The Rig (OTR) simulator. The Workmain.cs inside the ConsoleApp folder is executed during the simulation for collecting the time series dataset of the drilling variables. Python Codes for Data Analysis and Modelling.zip -- This folder contains the codes for analysing the datasets collected from the OTR. Principal component analysis is used for detecting kicks and principal component regression is used for predicting formation pressure. The detail description and analysis of the data will be published later in a complete manuscript.