## 1 Introduction  
The integration of sensors and internet connectivity into everyday objects through the Internet of Things (IoT) is transforming industries and gaining widespread adoption. In essence, the IoT refers to a network of physical devices that exchange information over the internet, enabling Machine-to-Machine (M2M) communication. This capability enhances monitoring, management systems, and automation processes, thereby improving decision-making and enhancing user experiences [1, 2, 3]. IoT systems are built upon three primary layers: the sensor layer, the connectivity layer, and the cloud layer. The sensor layer collects real-time data, such as temperature, humidity, and power consumption, from the target environment. These data are then transmitted and stored either locally or in the cloud using internet connectivity [4]. In recent years, IoT-enabled systems combined with artificial intelligence (AI) have revolutionized environmental parameter monitoring and energy consumption forecasting [5]. These technologies enhance the efficiency of data collection and analysis, improving the accuracy of environmental monitoring while enabling predictive capabilities to optimize energy management [6]. The research underscores the importance of environmental conditions in workplace productivity and employee well-being. [PERSON] and [PERSON] [7] found that office environments significantly influence employee satisfaction, comfort, mental and physical health, and productivity. [PERSON] et al. [8] highlighted that AI-enabled IoT systems provide valuable insights into environmental data, ensuring optimal conditions to enhance productivity. Temperature and humidity, in particular, play a critical role in workplace performance. A study reported that call center workers' productivity declined by 2.4% for every degree Celsius increase within 21.9 \({}^{\circ}\)C to 28.5 \({}^{\circ}\)C, with a 5-7% reduction when temperatures exceeded 25 \({}^{\circ}\)C [9]. Similarly, high humidity negatively affects worker output, with surveys indicating a 14% decrease in tasks completed per hour on humid days [10]. These findings emphasize the critical need for precise environmental monitoring and control. Leveraging AI-driven IoT systems for forecasting environmental conditions can significantly enhance workplace productivity by maintaining optimal conditions and improving energy management strategies. [PERSON] et al. [11] proposed an IoT-based Recurrent Neural Network (RNN) with backpropagation for agricultural use. Their study shows that when the RNN model is paired with IoT-based continued learning processes, model performance improves significantly; however, this study is mainly limited to agricultural uses. [PERSON] et al. [12] proposed an LSTM-based approach for accurately forecasting energy consumption patterns in industrial settings. While their model demonstrated lower Root Mean Squared Error (RMSE) compared to backpropagation neural networks, Autoregressive Moving Average (ARMA), and autoregressive fractional integrated moving average (ARFIMA) models, it lacked IoT integration, limiting its applicability and scalability. [PERSON] et al. [13] conducted a comparative analysis of various Machine Learning (ML) and deep learning models for real-life energy consumption forecasting. Their findings highlighted LSTM as the most accurate model, delivering lower error rates than other techniques. However, this study focused exclusively on load forecasting for residential houses and did not incorporate environmental monitoring or IoT-enabled data collection, which are critical for training future models and enhancing their performance. [PERSON] et al. [14] explored energy management strategies using LSTM, GRU, and Drop-GRU models, with Drop-GRU outperforming the others. Despite its promising results, the study omitted environmental monitoring and IoT-based data collection, factors that play a vital role in future refining model performance and ensuring its adaptability to diverse conditions. [PERSON] et al.