Kitchen object detection is an emerging field that leverages machine learning techniques to improve safety, efficiency, and automation in domestic and commercial kitchen environments. This study proposes a novel approach to detect key kitchen objects, specifically humans, fire, and groceries, using a Long Short-Term Memory (LSTM) network. The model focuses on temporal dependencies and sequential data to identify the presence and behavior of these objects in real-time. By utilizing video footage or image sequences, the LSTM can effectively learn patterns over time, allowing for more accurate detection compared to static image models. This approach is particularly useful for detecting dynamic and time-sensitive events such as fire outbreaks or human activities in the kitchen. The model leverages pre-trained Convolutional Neural Networks (CNNs) for feature extraction, which are then fed into the LSTM for temporal analysis. The LSTM's ability to remember relevant information over time makes it suitable for environments like kitchens, where object interactions evolve sequentially. This framework has the potential to be applied in smart kitchens for enhanced monitoring, early hazard detection, and grocery inventory management. The key contributions of this work include: (1) the use of LSTM for sequential object detection in kitchen environments, (2) real-time detection and classification of humans, fire, and groceries, and (3) improved detection accuracy through temporal analysis of video frames. This system aims to increase safety by detecting fires and human presence while also streamlining kitchen operations through grocery identification and monitoring.