Sensing data remotely is getting more detailed and with smaller expenses now. Consequently, events may be detected offline or in real-time and fed into applications such as preparation, policymaking, natural hazards, environmental events, temporal based weather comfort, and emissions automatic monitoring and alert systems. In recent years, developments in wireless sensor networks and the Internet of Things (IoT) have allowed users to track the environment in even more high-resolution information. Air quality control and weather comfort are achieved by way of a distributed sensor network. Science and development programs involving dynamic structures are mostly conducted in cooperation with different institutions, engineers, and scientists. Various sections of the framework are built by multiple organizations situated in other geographic locations in such a partnership. Interest in Machine Learning (ML) based science and engineering methods have recently grown rapidly. This increasing enthusiasm stems from the joint production and usage of effective algorithms for analysis, the vast volumes of data accessible from experimental instruments and other sources, and the accomplishments recorded by researchers and the academic community. Contemporary ameliorations in the sizes of the gathered data by outer space expeditions have created the needed space for innovative analyzing and classification models. In this dissertation, structured ML prediction is considered and precisely, those involving sequential structure. It is focused on the relatively mature methods of Complex Event Processing (CEP), which is associated with the identification of complex events focused on domain specialist rules and trends, while the meta-learning LSTM and Constructive Knowledge-based Event (CKE) algorithms can provide better analyses of vast volumes of data within a small-time interval and supervised learning via structured machine-learning prediction. This dissertation focus on the specific issues prompted by structured outputs when the object involved in a ML task has a CEP, prediction, or multi-class classification where the goal is to predict several outcomes from some set to an instance. I present our structured ML approaches to learning specific similarity measures for change-point detection. Analyzing the environmental and interplanetary trajectory is a big part of the research tasks as rapidly linked tools, and sensory devices become part of our everyday lives. The high-velocity knowledge flow sea is rising. This vast amount of high-rate data generated demands rapid insight in numerous applications such as the IoT, energy storage, etc. This produces the need for CEP frameworks, utilizing articulate state-of-the-art approaches to collect qualitative details. The Mission of Cassini, as an illustration, generated more than 630 gigabytes of research-based data that include 450,000 taken images. ML assists the experts and researchers with data enclosed by this vast extent. This dissertation uses the Cassini dataset as a particular instance of analysis to illustrate the remarkable capabilities of introducing Artificial Intelligence (AI) among space missions to extend further intelligent computing. It is intended to consider exploiting Deep Learning (DL) on the space mission's evolution platforms, offering higher efficiency and reliability. Using DL classifiers with diverse data volume access, it is illustrated that incorporating the collected spacecraft data with machine-learning approaches, which is fundamental for obtaining scientific significance. Based on these findings, the provided models on incorporating space sensed data into AI scope earmarking supervised classification concerning planetary data, which expresses a path incorporating Cassini spacecraft mission data into ML. The techniques of DL can be utilized to evolve intelligent solutions. Over the past several years, massive progress in AI techniques with special attention on neural networks has appeared globally. There has been a generous dash in the AI scope to provide robust solutions in numerous fields. Models focused on RNN with Long Short-Term Memory (LSTM), Bidirectional LSTMs, and Gated Recurrent Unite (GRU) are exceptional in learning sequences and capable of capturing long-range dependencies in the temporal data collection. In this analysis, various LSTM and Gated Recurrent Unit (GRU) models are used to model the Cassini–Huygens' outer space mission. The "learning" particular aspect of ML indicates the potency of an algorithm to detect data patterns to enhance the results, i.e., to utilize the available data to inspect and predict the unrenowned. ML so far has many implementations in video surveillance, online banking, aviation, product recommendations, and so on. Moreover, the technology is anticipated to leverage future space exploration since it can process massive data volumes, foretell spacecraft status, and detect patterns in the analyzed images. ML could empower cost-effectiveness, science profit, and dependability of missions in outer space. This work employs environmental and planetary mission datasets to build creative classifiers. It is also demonstrated that amalgamating the generated spacecraft data facilitates ML approaches performance and delineation, which is fundamental for obtaining systematic purport. Based on these accumulations of findings, an approach amalgamating space generated data into ML methods designating supervised classification within the scope of planetary data context. These reached results provide a footpath for analyzing environmental data and incorporating data of planetary missions and DL algorithms to grant the computers the ability to impart data to create categorize and predictions swiftly and with high accuracy. A cutting-edge predictive CEP system has been established that use historical knowledge to diagnose unique and complicated incidents. To use historical knowledge effectively, the method uses N-dimensional, historically matched sequence space. The prediction may then be made by addressing the set of queries over historical sequence space. This dissertation seeks to apply AI to determine which results will fit a given task. However, I am not investigating various types of AI methodologies, instead I am using these efficient solutions to identify specific phenomenon of the systems I have studied.